Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict? (2402.15467v1)
Abstract: The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI. Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in GenAI.
- Producers equilibria and dynamics in engagement-driven recommender systems. arXiv preprint arXiv:2401.16641, 2024.
- Diversified recommendations for agents with adaptive preferences. arXiv preprint arXiv:2210.07773, 2022.
- Bitcoin: A natural oligopoly. Management Science, 68(7):4755–4771, 2022.
- Shapley facility location games. In International Conference on Web and Internet Economics, pages 58–73. Springer, 2017.
- A game-theoretic approach to recommendation systems with strategic content providers. Advances in Neural Information Processing Systems, 31, 2018.
- Content provider dynamics and coordination in recommendation ecosystems. Advances in Neural Information Processing Systems, 33:18931–18941, 2020.
- On the stability of iterative retraining of generative models on their own data. arXiv preprint arXiv:2310.00429, 2023.
- Modeling recommender ecosystems: Research challenges at the intersection of mechanism design, reinforcement learning and generative models. arXiv preprint arXiv:2309.06375, 2023.
- Bandit learning in concave n-person games. Advances in Neural Information Processing Systems, 31, 2018.
- Large language models suffer from their own output: An analysis of the self-consuming training loop. arXiv preprint arXiv:2311.16822, 2023.
- Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
- An attraction–selection–attrition theory of online community size and resilience. Mis Quarterly, 38(3):699–729, 2014.
- Doubly optimal no-regret learning in monotone games. arXiv preprint arXiv:2301.13120, 2023.
- The winner of a prestigious japanese literary award has confirmed ai helped write her book. https://www.cnn.com/2024/01/19/style/rie-kudan-akutagawa-prize-chatgpt/index.html, January 2024.
- A generalized tullock contest. Public Choice, 147:413–420, 2011.
- Asymmetric contests with general technologies. Economic theory, 26:923–946, 2005.
- Preference dynamics under personalized recommendations. In Proceedings of the 23rd ACM Conference on Economics and Computation, pages 795–816, 2022.
- Recommender systems as dynamical systems: Interactions with viewers and creators. In Workshop on Recommendation Ecosystems: Modeling, Optimization and Incentive Design, 2024.
- Gerard Debreu. A social equilibrium existence theorem. Proceedings of the National Academy of Sciences, 38(10):886–893, 1952.
- A survey of experimental research on contests, all-pay auctions and tournaments. Experimental Economics, 18:609–669, 2015.
- Generative ai enhances individual creativity but reduces the collective diversity of novel content. Available at SSRN, 2023.
- Art and the science of generative ai. Science, 380(6650):1110–1111, 2023.
- On the convergence of regret minimization dynamics in concave games. In Proceedings of the forty-first annual ACM symposium on Theory of computing, pages 523–532, 2009.
- Christian Ewerhart. Mixed equilibria in tullock contests. Economic Theory, 60:59–71, 2015.
- Christian Ewerhart. The lottery contest is a best-response potential game. Economics Letters, 155:168–171, 2017. ISSN 0165-1765. doi: https://doi.org/10.1016/j.econlet.2017.03.030. URL https://www.sciencedirect.com/science/article/pii/S0165176517301325.
- Ky Fan. Fixed-point and minimax theorems in locally convex topological linear spaces. Proceedings of the National Academy of Sciences, 38(2):121–126, 1952.
- Abheek Ghosh. Best-response dynamics in tullock contests with convex costs. arXiv preprint arXiv:2310.03528, 2023.
- Irving L Glicksberg. A further generalization of the kakutani fixed theorem, with application to nash equilibrium points. Proceedings of the American Mathematical Society, 3(1):170–174, 1952.
- Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
- Modeling content creator incentives on algorithm-curated platforms. In The Eleventh International Conference on Learning Representations, 2022.
- Incentivizing high-quality content in online recommender systems. arXiv preprint arXiv:2306.07479, 2023.
- Clickbait vs. quality: How engagement-based optimization shapes the content landscape in online platforms. In Proceedings of the ACM Web Conference 2024, 2024.
- Supply-side equilibria in recommender systems. arXiv preprint arXiv:2206.13489, 2022.
- Supply-side equilibria in recommender systems. Advances in Neural Information Processing Systems, 2023.
- Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020.
- Bitcoin: An axiomatic approach and an impossibility theorem. American Economic Review: Insights, 2(3):269–286, 2020.
- Optimizing long-term social welfare in recommender systems: A constrained matching approach. In International Conference on Machine Learning, pages 6987–6998. PMLR, 2020.
- Dennis C Mueller. Public choice III. Cambridge University Press, 2003.
- John F Nash Jr. Equilibrium points in n-person games. Proceedings of the national academy of sciences, 36(1):48–49, 1950.
- A general analysis of rent-seeking games. Public choice, 73(3):335–350, 1992.
- J Ben Rosen. Existence and uniqueness of equilibrium points for concave n-person games. Econometrica: Journal of the Econometric Society, pages 520–534, 1965.
- George Selgin. Gresham’s law. Handbook of the History of Money and Currency, pages 199–219, 2020.
- Ronald William Shephard. Theory of cost and production functions. Princeton University Press, 2015.
- Ben Sisario. Universal music group pulls songs from tiktok. https://www.nytimes.com/2024/02/01/arts/music/universal-group-tiktok-music.html, January 2024.
- Stefan Szymanski. The economic design of sporting contests. Journal of economic literature, 41(4):1137–1187, 2003.
- Content creators’ participation in the creator economy: Examining the effect of creators’ content sharing frequency on user engagement behavior on digital platforms. Journal of Retailing and Consumer Services, 73:103357, 2023.
- Bandit learning in convex non-strictly monotone games. arXiv preprint arXiv:2009.04258, 2020.
- Gordon Tullock. Efficient rent seeking. In Toward a theory of the rent-seeking society. College Station, TX: Texas A&M University Press, 1980.
- The dark side of generative artificial intelligence: A critical analysis of controversies and risks of chatgpt. Entrepreneurial Business and Economics Review, 11(2):7–30, 2023.
- Written by chatgpt, illustrated by midjourney: generative ai for content marketing. Asia Pacific Journal of Marketing and Logistics, 35(8):1813–1822, 2023.
- Learning from a learning user for optimal recommendations. In International Conference on Machine Learning, pages 25382–25406. PMLR, 2022a.
- Learning the optimal recommendation from explorative users. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 9457–9465, 2022b.
- How bad is top-k𝑘kitalic_k recommendation under competing content creators? In International Conference on Machine Learning, pages 39674–39701. PMLR, 2023a.
- Rethinking incentives in recommender systems: Are monotone rewards always beneficial? Advances in Neural Information Processing Systems, 2023b.
- Online learning in a creator economy. arXiv preprint arXiv:2305.11381, 2023.
- Fan Yao (23 papers)
- Chuanhao Li (32 papers)
- Denis Nekipelov (16 papers)
- Hongning Wang (107 papers)
- Haifeng Xu (95 papers)