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Empirical Game-Theoretic Analysis: A Survey (2403.04018v1)

Published 6 Mar 2024 in cs.GT

Abstract: In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes not from declarative representation, but is derived by interrogation of a procedural description of the game environment. The motivation for developing this approach was to enable game-theoretic reasoning about strategic situations too complex for analytic specification and solution. Since its introduction over twenty years ago, EGTA has been applied to a wide range of multiagent domains, from auctions and markets to recreational games to cyber-security. We survey the extensive methodology developed for EGTA over the years, organized by the elemental subproblems comprising the EGTA process. We describe key EGTA concepts and techniques, and the questions at the frontier of EGTA research. Recent advances in machine learning have accelerated progress in EGTA, and promise to significantly expand our capacities for reasoning about complex game situations.

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References (181)
  1. Approximating Nash equilibria for black-box games: A Bayesian optimization approach. Technical report, MIT CSAIL, 2018. arXiv 1804.10586v2.
  2. Empirical mechanism design: Designing mechanisms from data. In 35th Conference on Uncertainty in Artificial Intelligence, 2019.
  3. Improved algorithms for learning equilibria in simulation-based games. In 19th International Conference on Autonomous Agents and Multi-Agent Systems, pages 79–87, 2020.
  4. Learning competitive equilibria in noisy combinatorial markets. In 20th International Conference on Autonomous Agents and Multi-Agent Systems, pages 1446–1448, 2021.
  5. Empirical game-theoretic models: Constrained equilibrium and simulations. Technical report, State University of New York at Stonybrook, 2000.
  6. Derivative-Free and Blackbox Optimization. Springer, 2017.
  7. Evaluating practical negotiating agents: Results and analysis of the 2011 international competition. Artificial Intelligence, 198:73–103, 2013.
  8. Re-evaluating evaluation. In 32nd Annual Conference on Neural Information Processing Systems, pages 3272–3283, 2018.
  9. Open-ended learning in symmetric zero-sum games. In 36th International Conference on Machine Learning, pages 434–443, 2019.
  10. Some results on lipschitz quasi-arithmetic means. In IFSA-EUSFLAT 2009: Proceedings of the 2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, pages 1370–1375. EUSFLAT, 2009.
  11. George Bennett. Probability inequalities for the sum of independent random variables. Journal of the American Statistical Association, 57(297):33–45, 1962.
  12. Learning equilibria in symmetric auction games using artificial neural networks. Nature Machine Intelligence, 3:687–695, 2021.
  13. Policy space response oracles: A survey. arXiv preprint arXiv:2403.02227, 2024.
  14. Evolutionary dynamics of multi-agent learning: A survey. Journal of Artificial Intelligence Research, 53:659–697, 2015.
  15. Learning through reinforcement and replicator dynamics. Journal of Economic Theory, 77(1):1–14, 1997.
  16. Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press, 2013.
  17. Erik Brinkman. Understanding Financial Market Behavior through Empirical Game-Theoretic Analysis. PhD thesis, University of Michigan, 2018.
  18. Empirical mechanism design for optimizing clearing interval in frequent call markets. In 18th ACM Conference on Economics and Computation, pages 205–221, 2017.
  19. George W. Brown. Iterative solution of games by fictitious play. In T. C. Koopmans, editor, Activity Analysis of Production and Allocation, pages 374–376. Wiley, 1951.
  20. AIVAT: A new variance reduction technique for agent evaluation in imperfect information games. In 32nd AAAI Conference on Artificial Intelligence, 2018.
  21. EGTAOnline: An experiment manager for simulation-based game studies. In Francesca Giardini and Frédéric Amblard, editors, Multi-Agent-Based Simulation XIII, volume 7838 of Lecture Notes in Artificial Intelligence. Springer, 2013.
  22. Accounting for strategic response in an agent-based model of financial regulation. In 18th ACM Conference on Economics and Computation, pages 187–203, 2017.
  23. Strategic payment routing in financial credit networks. In 17th ACM Conference on Economics and Computation, pages 721–738, 2016.
  24. Cap-and-trade emissions regulation: A strategic analysis. In 28th International Joint Conference on Artificial Intelligence, pages 187–193, 2019.
  25. Pushing the limits of rational agents: The trading agent competition for supply chain management. AI Magazine, 31(2):63–80, 2010.
  26. Sharp uniform convergence bounds through empirical centralization. In Advances in Neural Information Processing Systems, volume 33, pages 15123–15132, 2020.
  27. Computational and data requirements for learning generic properties of simulation-based games. arXiv preprint arXiv:2208.06400, 2022.
  28. Learning properties in simulation-based games. In Noa Agmon, Bo An, Alessandro Ricci, and William Yeoh, editors, Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023, London, United Kingdom, 29 May 2023 - 2 June 2023, pages 272–280. ACM, 2023. doi: 10.5555/3545946.3598647. URL https://dl.acm.org/doi/10.5555/3545946.3598647.
  29. Real world games look like spinning tops. In 34th Annual Conference on Neural Information Processing Systems, 2020.
  30. Strategic formation of credit networks. ACM Transactions on Internet Technology, 15(1):3:1–41, 2015.
  31. The complexity of computing a Nash equilibrium. SIAM Journal on Computing, 39(1):195–259, 2009.
  32. Bootstrap Methods and their Application. Cambridge University Press, 1997.
  33. Online double oracle. Transactions on Machine Learning Research, October 2022.
  34. Learning graphical game models. In 21st International Joint Conference on Artificial Intelligence, pages 116–121, Pasadena, 2009.
  35. Optimal auctions through deep learning. In 36th International Conference on Machine Learning, pages 1706–1715, 2019.
  36. Learning equilibria of games via payoff queries. In 14th ACM Conference on Electronic Commerce, 2013.
  37. Learning equilibria of games via payoff queries. Journal of Machine Learning Research, 16:1305–1344, 2015.
  38. Learning and solving many-player games through a cluster-based representation. In 24th Conference on Uncertainty in Artificial Intelligence, pages 187–195, Helsinki, 2008.
  39. The Theory of Learning in Games. MIT Press, 1998.
  40. Learning game representations from data using rationality constraints. In 26th Conference on Uncertainty in Artificial Intelligence, pages 185–192, 2010.
  41. Roman Garnett. Bayesian Optimization. Cambridge University Press, 2023. doi: 10.1017/9781108348973.
  42. Learning parameterized families of games. In 22nd International Conference on Autonomous Agents and Multi-Agent Systems, pages 1044–1052, 2023.
  43. Equilibrium approximation in simulation–based extensive-form games. In 10th International Conference on Autonomous Agents and Multi-Agent Systems, pages 199–206, 2011.
  44. Game-theoretic analysis of development practices: Challenges and opportunities. Journal of Systems and Software, 159, 2020.
  45. Herbert Gintis. Game Theory Evolving. Princeton University Press, 2nd edition, 2009.
  46. The complexity of the homotopy method, equilibrium selection, and Lemke-Howson solutions. ACM Transactions on Economics and Computation, 1(2):9, 2013.
  47. Approximating equilibria in sequential auctions with incomplete information and multi-unit demand. In Advances in Neural Information Processing Systems, 2012.
  48. Shopbots and pricebots. In 16th International Joint Conference on Artificial Intelligence, pages 506–511, 1999.
  49. Strategic pricebot dynamics. In 1st ACM Conference on Electronic Commerce, pages 58–67, 1999.
  50. A General Theory of Equilibrium Selection in Games. MIT Press, 1988.
  51. J. Hofbauer and K. Sigmund. Evolutionary Games and Population Dynamics. Cambridge University Press, 1998.
  52. Learning the structure and parameters of large-population graphical games from behavioral data. Journal of Machine Learning Research, 16:1157–1210, 2015.
  53. Bounding regret in empirical games. In 34th AAAI Conference on Artificial Intelligence, pages 4280–4287, 2020.
  54. Patrick R. Jordan. Practical Strategic Reasoning with Applications in Market Games. PhD thesis, University of Michigan, 2010.
  55. Generalization risk minimization in empirical game models. In 8th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 553–560, 2009.
  56. Empirical game-theoretic analysis of the TAC supply chain game. In 6th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 1188–1195, 2007.
  57. Searching for approximate equilibria in empirical games. In 7th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 1063–1070, 2008.
  58. Strategy exploration in empirical games. In 9th International Conference on Autonomous Agents and Multi-Agent Systems, pages 1131–1138, 2010a.
  59. Strategy and mechanism lessons from the first ad auctions trading agent competition. In 11th ACM Conference on Electronic Commerce, pages 287–296, 2010b.
  60. Auction analysis by normal form game approximation. In IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pages 447–450, 2008.
  61. Michael Kearns. Graphical games. In Nisan et al. [2007], pages 159–180.
  62. Price and niche wars in a free-market economy of software agents. Artificial Life, 4:1–23, 1998.
  63. Space debris removal: A game theoretic analysis. Games, 7:20:1–20:18, 2016.
  64. Exploiting extensive-form structure in empirical game-theoretic analysis. In 18th Conference on Web and Internet Economics, pages 132–149, 2022.
  65. A unified game-theoretic approach to multiagent reinforcement learning. In 31st Annual Conference on Neural Information Processing Systems, pages 4190–4203, 2017.
  66. A perspective on the use of control variables to increase the efficiency of Monte Carlo simulations. Management Science, 27(3):322–335, 1981.
  67. Pierre L’Ecuyer. Efficiency improvement and variance reduction. In Twenty-Sixth Winter Simulation Conference, pages 122–132, 1994.
  68. Multi-agent reinforcement learning in sequential social dilemmas. In 16th International Conference on Autonomous Agents and Multi-Agent Systems, pages 464–473, 2017.
  69. Markov Chains and Mixing Times. American Mathematical Society, 2006.
  70. Essentials of Game Theory: A Concise, Multidisciplinary Introduction. Morgan & Claypool, 2008.
  71. Solving large-scale pursuit-evasion games using pre-trained strategies. In 37th AAAI Conference on Artificial Intelligence, pages 11586–11594, 2023a.
  72. Structure learning for approximate solution of many-player games. In 34th AAAI Conference on Artificial Intelligence, pages 2119–2127, 2020.
  73. Evolution strategies for approximate solution of Bayesian games. In 35th AAAI Conference on Artificial Intelligence, pages 5531–5540, 2021.
  74. Combining tree-search, generative models, and Nash bargaining concepts in game-theoretic reinforcement learning. arXiv preprint arXiv:2302.00797, 2023b.
  75. The spoofing resistance of frequent call markets. In 21st International Conference on Autonomous Agents and Multi-Agent Systems, pages 825–832, 2022.
  76. Learning probably approximately correct maximin strategies in simulation-based games with infinite strategy spaces. In 19th International Conference on Autonomous Agents and Multi-Agent Systems, 2020.
  77. Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers. In 38th International Conference on Machine Learning, pages 7480–7491, 2021.
  78. J. Maynard Smith and G. R. Price. The logic of animal conflicts. Nature, 246:15–18, 1973.
  79. A strategic analysis of portfolio compression. In 2nd International Conference on Artificial Intelligence in Finance, 2021.
  80. An agent-based model of strategic adoption of real-time payments. In 2nd International Conference on Artificial Intelligence in Finance, 2021.
  81. Pipeline PSRO: A scalable approach for finding approximate Nash equilibria in large games. In 34th Annual Conference on Neural Information Processing Systems, 2020.
  82. Anytime PSRO for two-player zero-sum games. In AAAI-22 Workshop on Reinforcement Learning in Games, 2022.
  83. Team-PSRO for learning approximate TMECor in large team games via cooperative reinforcement learning. In 37th Annual Conference on Neural Information Processing Systems, 2023.
  84. Catherine C. McGeoch. A Guide to Experimental Algorithmics. Cambridge University Press, 2012.
  85. Planning in the presence of cost functions controlled by an adversary. In 20th International Conference on Machine Learning, pages 536–543, 2003.
  86. Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press, 2007.
  87. A generalized training approach for multiagent learning. In 8th International Conference on Learning Representations, 2020.
  88. Learning equilibria in mean-field games: Introducing mean-field PSRO. In 21st International Conference on Autonomous Agents and Multi-Agent Systems, pages 926–934, 2022.
  89. Kumpati S. Narendra and Mandayam A. L. Thathachar. Learning Automata: An Introduction. Prentice Hall, 1989.
  90. John F. Nash. Equilibrium points in n𝑛nitalic_n-person games. Proceedings of the National Academy of Sciences, 36(1):48–49, 1950.
  91. Algorithmic Game Theory. Cambridge University Press, 2007.
  92. α𝛼\alphaitalic_α-Rank: Multi-agent evaluation by evolution. Scientific Reports, 9, 2019.
  93. Navigating the landscape of multiplayer games. Nature Communications, 11(5603), 2020.
  94. Modelling behavioural diversity for learning in open-ended games. In 38th International Conference on Machine Learning, pages 8514–8524, 2021.
  95. A novel method for automatic strategy acquisition in n𝑛nitalic_n-player non-zero-sum games. In 5th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 705–712, 2006.
  96. Steve Phelps. An empirical game-theoretic analysis of the dynamics of cooperation in small groups. Journal of Artificial Societies and Social Simulation, 19(2), 2016.
  97. An evolutionary game-theoretic comparison of two double-auction market designs. In Agent-Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems, volume 3435 of Lecture Notes in Computer Science, pages 101–114. Springer, 2005.
  98. A novel method for automatic strategy acquisition and its application to a double-auction market game. IEEE Transactions on Systems, Man, and Cybernetics: Part B, 40:668–674, 2010a.
  99. Evolutionary mechanism design: A review. Autonomous Agents and Multi-Agent Systems, 21:237–264, 2010b.
  100. A Bayesian optimization approach to find Nash equilibria. Journal of Global Optimization, 73:171–192, 2019.
  101. Bayes-relational learning of opponent models from incomplete information in no-limit poker. In 23rd AAAI Conference on Artificial Intelligence, pages 1485–1486, 2008.
  102. An evolutionary game-theoretic analysis of poker strategies. Entertainment Computing, 1(1):39–45, 2009.
  103. A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning. In 34th Annual Conference on Neural Information Processing Systems, 2020.
  104. Ji Qi and Carmine Ventre. Accounting for strategic response in limit order book dynamics. In 24th International Conference on Principles and Practice of Multi-Agent Systems, pages 630–639, Valencia, 2022.
  105. Sheldon M. Ross. Simulation. Academic Press, third edition, 2002.
  106. Multiagent evaluation under incomplete information. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
  107. Introduction to Modern Information Retrieval. McGraw-Hill, New York, NY, 1983.
  108. Coupled replicator equations for the dynamics of learning in multiagent systems. Physical Review E, 67(1), jan 2003.
  109. Replicator dynamics. Journal of Theoretical Biology, 100(3):533 – 538, 1983.
  110. Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning. In 8th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 249–256, 2009.
  111. Learning improved entertainment trading strategies for the TAC travel game. In Esther David, Enrico Gerding, David Sarne, and Onn Shehory, editors, Agent-Mediated Electronic Commerce: Designing Trading Strategies and Mechanisms for Electronic Markets, volume 59 of Lecture Notes in Business Information Processing. Springer-Verlag, 2010.
  112. Statistics is Easy! Morgan and Claypool, second edition, 2011.
  113. Stability effects of arbitrage in exchange-traded funds. In 2nd International Conference on Artificial Intelligence in Finance, 2021.
  114. Learning to manipulate a financial benchmark. In 4th International Conference on Artificial Intelligence in Finance, pages 592–600, 2023.
  115. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419):1140–1144, 2018.
  116. Strategic knowledge transfer. Journal of Machine Learning Research, 24(233):1–96, 2023.
  117. Efficient statistical methods for evaluating trading agent performance. In 22nd National Conference on Artificial Intelligence, pages 770–775, 2007.
  118. Learning deviation payoffs in simulation-based games. In 33rd AAAI Conference on Artificial Intelligence, pages 2173–2180, 2019.
  119. Using tabu best-response search to find pure strategy Nash equilibria in normal form games. In 4th International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 1023–1029, 2005.
  120. TAC AdX’14: Autonomous agents for realtime ad exchange. In 14th International Conference on Autonomous Agents and Multi-Agent Systems, pages 1111–1119, 2015.
  121. No-regret sample-efficient Bayesian optimization for finding Nash equilibria with unknown utilities. In 26th International Conference on Artificial Intelligence and Statistics, 2023.
  122. P. Taylor and L. Jonker. Evolutionarily stable strategies and game dynamics. Mathematical Biosciences, 40:145–156, 1978.
  123. High-performance bidding agents for the continuous double auction. In 3rd ACM Conference on Electronic Commerce, pages 206–209, 2001.
  124. Leigh Tesfatsion. Agent-based computational economics: A constructive approach to economic theory. In Leigh Tesfatsion and Kenneth L. Judd, editors, Handbook of Computational Economics. Elsevier, 2006.
  125. What evolutionary game theory tells us about multiagent learning. Artificial Intelligence, 171(7):406–416, 2007.
  126. Towards a relation between learning agents and evolutionary dynamics. In 14th Belgium-Netherlands Conference on Artificial Intelligence BNAIC 2002, pages 315–322, 2002.
  127. A selection-mutation model for q-learning in multi-agent systems. In The Second International Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS 2003, July 14-18, 2003, Melbourne, Victoria, Australia, Proceedings, pages 693–700, 2003.
  128. A generalised method for empirical game theoretic analysis. In 17th International Conference on Autonomous Agents and Multi-Agent Systems, pages 77–85, 2018a.
  129. Symmetric decomposition of asymmetric games. Scientific Reports, 8(1):1015, 2018b.
  130. Bounds and dynamics for empirical game theoretic analysis. Autonomous Agents and Multi-Agent Systems, 34:7, 2020.
  131. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 2019.
  132. Yevgeniy Vorobeychik. Probabilistic analysis of simulation-based games. ACM Transactions on Modeling and Computer Simulation, 20(3):16:1–25, 2010.
  133. Mechanism design based on beliefs about responsive play (position paper). In ACM EC-06 Workshop on Alternative Solution Concepts for Mechanism Design, Ann Arbor, MI, 2006.
  134. Empirical mechanism design: Methods, with application to a supply chain scenario. In 7th ACM Conference on Electronic Commerce, pages 306–315, 2006.
  135. Learning payoff functions in infinite games. Machine Learning, 67:145–168, 2007.
  136. Constrained automated mechanism design for infinite games of incomplete information. Autonomous Agents and Multi-Agent Systems, 25:313–351, 2012.
  137. Zbynek Šidák. Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62(318):626–633, 1967.
  138. Latency arbitrage in fragmented markets: A strategic agent-based analysis. Algorithmic Finance, 5:69–93, 2016.
  139. Strategic market choice: Frequent call markets vs. continuous double auctions for fast and slow traders. In Third EAI Conference on Auctions, Market Mechanisms, and Their Applications, 2015.
  140. An empirical game-theoretic analysis of price discovery in prediction markets. In 25th International Joint Conference on Artificial Intelligence, pages 510–516, 2016.
  141. Welfare effects of market making in continuous double auctions. Journal of Artificial Intelligence Research, 59:613–650, 2017.
  142. William E. Walsh. Market Protocols for Decentralized Supply Chain Formation. PhD thesis, University of Michigan, 2001.
  143. Combinatorial auctions for supply chain formation. In 2nd ACM Conference on Electronic Commerce, pages 260–269, 2000.
  144. Analyzing complex strategic interactions in multi-agent systems. In AAAI-02 Workshop on Game-Theoretic and Decision-Theoretic Agents, 2002.
  145. Choosing samples to compute heuristic-strategy Nash equilibrium. In AAMAS-03 Workshop on Agent-Mediated Electronic Commerce, 2003.
  146. Spoofing the limit order book: A strategic agent-based analysis. Games, 12(2), 2021.
  147. Empirical game-theoretic analysis for mean-field games. In 22nd International Conference on Autonomous Agents and Multi-Agent Systems, pages 1025–1033, 2023a.
  148. Regularization for strategy exploration in empirical game-theoretic analysis. arXiv preprint arXiv:2302.04928, 2023b.
  149. Generalized response objectives for strategy exploration in empirical game-theoretic analysis. In 23rd International Conference on Autonomous Agents and Multi-Agent Systems, 2024.
  150. Evaluating strategy exploration in empirical game-theoretic analysis. In 21st International Conference on Autonomous Agents and Multi-Agent Systems, pages 1346–1354, 2022.
  151. Deep reinforcement learning for green security games with real-time information. In 33rd AAAI Conference on Artificial Intelligence, pages 1401–1408, 2019.
  152. Christopher J. C. H. Watkins and Peter Dayan. Technical note q-learning. Machine Learning, 8:279–292, 1992.
  153. Computational rationalization: The inverse equilibrium problem. In 28th International Conference on Machine Learning, pages 1169–1176, 2011.
  154. Jorgen Weibull. Evolutionary Game Theory. MIT Press, 1997.
  155. Michael P. Wellman. Methods for empirical game-theoretic analysis (extended abstract). In 21st National Conference on Artificial Intelligence, pages 1552–1556, 2006.
  156. Michael P. Wellman. Putting the agent in agent-based modeling. Autonomous Agents and Multi-Agent Systems, 30:1175–1189, 2016.
  157. Michael P. Wellman. Economic reasoning from simulation-based game models. Œconomia, 10:257–278, 2020.
  158. Exploring bidding strategies for market-based scheduling. In 4th ACM Conference on Electronic Commerce, pages 115–124, 2003.
  159. Strategic interactions in a supply chain game. Computational Intelligence, 21:1–26, 2005a.
  160. Approximate strategic reasoning through hierarchical reduction of large symmetric games. In 20th National Conference on Artificial Intelligence, pages 502–508, 2005b.
  161. Autonomous Bidding Agents: Strategies and Lessons from the Trading Agent Competition. MIT Press, 2007.
  162. Bidding strategies for simultaneous ascending auctions. B. E. Journal of Theoretical Economics (Topics), 8(1), 2008.
  163. Analyzing incentives for protocol compliance in complex domains: A case study of introduction-based routing. In 12th Workshop on the Economics of Information Security, 2013.
  164. Self-confirming price-prediction strategies for simultaneous one-shot auctions. Games and Economic Behavior, 201:339–372, 2017.
  165. Empirical game-theoretic analysis for adaptive cyber-defense. In Sushil Jajodia, George Cybenko, Peng Liu, Cliff Wang, and Michael P. Wellman, editors, Adversarial and Uncertain Reasoning for Adaptive Cyber Defense, volume 11830 of Lecture Notes in Computer Science, pages 112–128. Springer, 2019.
  166. An algorithm for computing stochastically stable distributions with applications to multiagent learning in repeated games. In UAI ’05, Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, Edinburgh, Scotland, July 26-29, 2005, pages 623–632. AUAI Press, 2005.
  167. Data structures for deviation payoffs. In 22nd International Conference on Autonomous Agents and Multi-Agent Systems, pages 670–678, 2023.
  168. Scaling simulation-based game analysis through deviation-preserving reduction. In 11th International Conference on Autonomous Agents and Multi-Agent Systems, pages 931–938, 2012.
  169. Bootstrap statistics for empirical games. In 13th International Conference on Autonomous Agents and Multi-Agent Systems, pages 597–604, 2014.
  170. A regression approach for modeling games with many symmetric players. In 32nd AAAI Conference on Artificial Intelligence, pages 1266–1273, 2018.
  171. Resolving social dilemmas through reward transfer commitments. In AAMAS-23 Workshop on Adaptive Learning Agents, 2023.
  172. Predicting human behavior in unrepeated, simultaneous-move games. Games and Economic Behavior, 106:16–37, 2017.
  173. Iterated deep reinforcement learning in games: History-aware training for improved stability. In 20th ACM Conference on Economics and Computation, pages 617–636, 2019.
  174. Decentralized role assignment in multi-agent teams via empirical game-theoretic analysis. arXiv preprint arXiv:2109.14755, 2021.
  175. Policy space diversity for non-transitive games. In 37th Annual Conference on Neural Information Processing Systems, 2023.
  176. H. Peyton Young. The evolution of conventions. Econometrica, 61(1):57–84, 1993.
  177. E. C. Zeeman. Population dynamics from game theory. In Z. Nitecki and C. Robinson, editors, Global Theory of Dynamical Systems, volume 819 of Lecture Notes in Mathematics, pages 471–497. Springer, 1980.
  178. E. C. Zeeman. Dynamics of the evolution of animal conflicts. Theoretical Biology, 89:249–270, 1981.
  179. Markups in double auction markets. Journal of Economic Dynamics and Control, 31:2984–3005, 2007.
  180. Computing optimal equilibria and mechanisms via learning in zero-sum extensive-form games. In 37th Annual Conference on Neural Information Processing Systems, 2023.
  181. Liquidity and solvency risks in financial networks. In 4th International Conference on Artificial Intelligence in Finance, pages 210–218, 2023.
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