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Generative AI Triggers Welfare-Reducing Decisions in Humans (2401.12773v1)
Published 23 Jan 2024 in econ.GN and q-fin.EC
Abstract: Generative AI is poised to reshape the way individuals communicate and interact. While this form of AI has the potential to efficiently make numerous human decisions, there is limited understanding of how individuals respond to its use in social interaction. In particular, it remains unclear how individuals engage with algorithms when the interaction entails consequences for other people. Here, we report the results of a large-scale pre-registered online experiment (N = 3,552) indicating diminished fairness, trust, trustworthiness, cooperation, and coordination by human players in economic twoplayer games, when the decision of the interaction partner is taken over by ChatGPT. On the contrary, we observe no adverse welfare effects when individuals are uncertain about whether they are interacting with a human or generative AI. Therefore, the promotion of AI transparency, often suggested as a solution to mitigate the negative impacts of generative AI on society, shows a detrimental effect on welfare in our study. Concurrently, participants frequently delegate decisions to ChatGPT, particularly when the AI's involvement is undisclosed, and individuals struggle to discern between AI and human decisions.
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[39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023). [3] Peng, C. et al. A study of generative large language model for medical research and healthcare. npj Digital Medicine 6, 210 (2023). [4] Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023). [5] Yan, M., Cerri, G. G. & Moraes, F. Y. Chatgpt and medicine: how ai language models are shaping the future and health related careers. Nature Biotechnology 41, 1657–1658 (2023). [6] Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Peng, C. et al. A study of generative large language model for medical research and healthcare. npj Digital Medicine 6, 210 (2023). [4] Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023). [5] Yan, M., Cerri, G. G. & Moraes, F. Y. Chatgpt and medicine: how ai language models are shaping the future and health related careers. Nature Biotechnology 41, 1657–1658 (2023). [6] Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60 (2023). [5] Yan, M., Cerri, G. G. & Moraes, F. Y. Chatgpt and medicine: how ai language models are shaping the future and health related careers. Nature Biotechnology 41, 1657–1658 (2023). [6] Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. 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[10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. 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How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. 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Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. 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[26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. 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Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). 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[11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). 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Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. 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How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). 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SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. 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We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. 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[27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. 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Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. 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Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. 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D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Yan, M., Cerri, G. G. & Moraes, F. Y. Chatgpt and medicine: how ai language models are shaping the future and health related careers. Nature Biotechnology 41, 1657–1658 (2023). [6] Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 0, eadi2336 (forthcoming). [7] Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. 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Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). 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Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). 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[38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. 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GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. 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SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. 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[38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. 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[28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bi, K. et al. Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533–538 (2023). [8] Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Abramoff, M. D. et al. Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial. npj Digital Medicine 6, 184 (2023). [9] Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. 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The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? 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Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. 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[28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). 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Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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[39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Noy, S. & Zhang, W. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. 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Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. 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Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. 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Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. 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SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. 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Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. 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Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. 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Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. 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SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. 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[27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). 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[42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). 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[42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). 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Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. 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How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. 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[27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). 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- Experimental evidence on the productivity effects of generative artificial intelligence. Science 381, 187–192 (2023). [10] Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Tricomi, E., Rangel, A., Camerer, C. F. & O’Doherty, J. P. Neural evidence for inequality-averse social preferences. Nature 463, 1089–1091 (2010). [11] Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003). [12] Fehr, E. & Fischbacher, U. Social norms and human cooperation. Trends in Cognitive Sciences 8, 185–190 (2004). [13] Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. 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The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Henrich, J. et al. In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review 91, 73–78 (2001). [14] Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). 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URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). 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Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. 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The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. 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[28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). 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Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Pataranutaporn, P., Liu, R., Finn, E. & Maes, P. Influencing human-ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence 5, 1076–1086 (2023). [15] Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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Bauer, K., Liebich, L., Hinz, O. & Kosfeld, M. Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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[39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. 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We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). 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URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994).
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ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). 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Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994).
- Decoding GPT’s hidden “rationality” of cooperation. SSRN Electronic Journal (2023). URL https://www.ssrn.com/abstract=4576036. [16] Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chen, Y., Liu, T. X., Shan, Y. & Zhong, S. The Emergence of Economic Rationality of GPT (2023). URL http://arxiv.org/abs/2305.12763. ArXiv:2305.12763 [econ, q-fin]. [17] Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. 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Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. 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[25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. 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Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. 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How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Guo, F. GPT Agents in Game Theory Experiments (2023). URL http://arxiv.org/abs/2305.05516. ArXiv:2305.05516 [econ, q-fin]. [18] Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Dargnies, M.-P., Hakimov, R. & Kübler, D. F. Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. SSRN Electronic Journal (2022). URL https://www.ssrn.com/abstract=4238275. [19] Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Chugunova, M. & Sele, D. We and it: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897 (2022). [20] Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. 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[26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). 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Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). 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[28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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[29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners (2023). URL https://doi.org/10.48550/arXiv.2306.04484. arXiv:2306.04484. [21] Candrian, C. & Scherer, A. Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). 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Rise of the machines: Delegating decisions to autonomous ai. Computers in Human Behavior 134, 107308 (2022). [22] von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. 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When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. 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The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). [35] Dvorak, F., Fischbacher, U., Fehrler, S. & Stumpf, R. Ai decisions in human interaction. (2023). URL https://osf.io/fvk2c/. [36] Holm, S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 65–70 (1979). [37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. 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The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). von Schenk, A., Klockmann, V. & Köbis, N. Social preferences toward humans and machines: A systematic experiment on the role of machine payoffs. Perspectives on Psychological Science 0, 17456916231194949 (forthcoming). URL https://doi.org/10.1177/17456916231194949. PMID: 37751604. [23] Kawaguchi, K. When will workers follow an algorithm? a field experiment with a retail business. Management Science 67, 1670–1695 (2021). [24] Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. 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[37] Similarweb: Chatgpt usage statistics (retrieved 2023-12-08). URL https://www.similarweb.com/website/chat.openai.com/. [38] Nerdynav: Chatgpt usage statistics (retrieved 2023-12-08). URL https://nerdynav.com/chatgpt-statistics/. [39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). 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Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. 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[39] Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. Risk and Temptation: A Meta‐study on Prisoner’s Dilemma Games. The Economic Journal 128, 3182–3209 (2017). [34] Dal Bó, P., Fréchette, G. R. & Kim, J. The determinants of efficient behavior in coordination games. Games and Economic Behavior 130, 352–368 (2021). 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[26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151, 90–103 (2019). [25] Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). URL https://chat.openai.com. [28] Güth, W., Schmittberger, R. & Schwarze, B. An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization 3, 367–388 (1982). [29] Berg, J., Dickhaut, J. & McCabe, K. Trust, reciprocity, and social history. Games and Economic Behavior 10, 122–142 (1995). [30] Lave, L. B. An empirical approach to the Prisoners’ Dilemma Game*. The Quarterly Journal of Economics 76, 424–436 (1962). [31] Johnson, N. D. & Mislin, A. A. Trust games: A meta-analysis. Journal of Economic Psychology 32, 865–889 (2011). [32] Oosterbeek, H., Sloof, R. & van de Kuilen, G. Cultural differences in ultimatum game experiments: Evidence from a meta-analysis. Experimental Economics 7, 171–188 (2004). [33] Mengel, F. 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M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Greiner, B., Grunwald, P., Lindner, T., Lintner, G. & Wiernsperger, M. Incentives, framing, and trust in algorithmic advice: An experimental study (2022). URL https://www.uibk.ac.at/smt/international-management/research/greiner-et-al_algorithmic-advice.pdf. [26] Zhou, W., Lin, M., Xiao, M. & Fang, L. Exploitation and exploration: Improving search precision on e-commerce platforms. Available at SSRN 3762144 (2021). [27] OpenAI. Chatgpt jan 30 version (2023). 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- Ishowo-Oloko, F. et al. Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation. Nature Machine Intelligence 1, 517–521 (2019). URL https://doi.org/10.1038/s42256-019-0113-5. [40] Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Leib, M., Köbis, N., Rilke, R. M., Hagens, M. & Irlenbusch, B. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994).
- Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty*. The Economic Journal uead056 (2023). URL https://doi.org/10.1093/ej/uead056. [41] Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Thielmann, I., Böhm, R., Ott, M. & Hilbig, B. E. Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994).
- Economic Games: An Introduction and Guide for Research. Collabra: Psychology 7, 19004 (2021). [42] Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994). Mehta, J., Starmer, C. & Sugden, R. The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994).
- The nature of salience: An experimental investigation of pure coordination games. The American Economic Review 84, 658–673 (1994).
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