Learning in Games

18 Jul 2023 08:34

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That is, learning in game theory, or at least contexts pretty close to it.

See also
• Collective Cognition
• Economics
• Evolutionary Economics
• Evolutionary Game Theory
• Learning Theory
• Low-Regret Learning
• Machine Learning, Statistical Inference and Induction
• Mean-Field Games the Minority Game
• Sequential Decisions Under Uncertainty
• Universal Prediction Algorithms

Recommended, big picture:
• Nicolo Cesa-Bianchi and Gabor Lugosi, Prediction, Learning, and Games
• Herbert Gintis, Game Theory Evolving: A Problem-Centered Introduction to Modeling Strategic Interaction
• Larry Samuelson, Evolutionary Games and Equilibrium Selection
• William Sandholm, Population Games and Evolutionary Dynamics

Recommended, close-ups:
• Jenna Bednar and Scott Page, "Can Game(s) Theory Explain Culture? The Emergence of Cultural Behavior Within Multiple Games", Rationality and Society 19 (2007): 65--97 [PDF preprint via Prof. Bednar]
• Lawrence E. Blume and David Easley
  • "If You're So Smart, Why Aren't You Rich? Belief Selection in Complete and Incomplete Markets", Econometrica 74 (2006): 929--966, SFI Working Paper 01-06-031
  • "Optimality and Natural Selection in Markets," SFI Working Paper 98-09-0 82
• Tilman Börgers and Rajiv Sarin, "Learning Through Reinforcement and Replicator Dynamics", Journal of Economic Theory 77 (1997): 1--14
• Vivek S. Borkar, "Reinforcement Learning in Markovian Evolutionary Games", Advances in Complex Systems 5 (2002): 55--72
• William A. Brock and Cars H. Hommes, "A Rational Route to Randomness", Econometrica 65 (1997): 1059--1095
• Christophe Chamley, Rational Herds: Economic Models of Social Learning
• Ido Erev and Alvin E. Roth, "Simple Reinforcement Learning Models and Reciprocation in the Prisoner's Dilemma Game", pp. 215--232 in Gigerenzer and selten (eds.), Bounded Rationality
• Ignacio Esponda, Demian Pouzo, Yuichi Yamamoto, "Asymptotic Behavior of Bayesian Learners with Misspecified Models", arxiv:1904.08551
• Dean P. Foster and H. Peyton Young, "Learning, hypothesis testing, and Nash equilibrium," Games and Economic Behavior 45 (2003): 73--96 [pdf]
• Joseph Y. Halpern, Rafael Pass, "Iterated Regret Minimization: A More Realistic Solution Concept", arxiv:0810.3023 [Somewhat astonishingly, does mention the huge literature on low-regret learning]
• Ariel Rubinstein, Modeling Bounded Rationality [Review: O docta simplicitas!]
• Timothy C. Salmon, "An Evaluation of Econometric Models of Adaptive Learning", Econometrica 69 (2001): 1597--1628
• José M. Vidal and Edmund H. Durfee, "Predicting the Expected Behavior of Agents That Learn About Agents: The CLRI Framework," cs.MA/0001008
• H. Peyton Young, Individual Strategy and Social Structure: An Evolutionary Theory of Institutions [Review: A Myopic (and Sometimes Blind) Eye on the Main Chance, or, the Origins of Custom]

To read:
• Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin, "A Stochastic View of Optimal Regret through Minimax Duality", arxiv:0903.5328
• James Bergin and Barton L. Lipman, "Evolution with State-Dependent Mutations," Econometrica 64 (1996): 943--956
• Andreas Blume, "A Learning-Efficiency Explanation of Structure in Language", Theory and Decision 57 (2004): 265--285
• Oliver Board, "Dynamic interactive epistemology", Games and Economic Behavior 49 (2004): 49--80
• Jacob W. Crandall and Michael A. Goodrich, "Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning", Machine Learning 82 (2011): 281--314
• Vincent P. Crawford, Miguel A. Costa-Gomes, and Nagore Iriberri, "Structural Models of Nonequilibrium Strategic Thinking: Theory, Evidence, and Applications", Journal of Economic Literature 51 (2013): 5--62
• Emilio De Santis and Carlo Marinelli, "Stochastic games with infinitely many interacting agents", math.PR/0505608 [Sounds very cool: "study a class of infinite-horizon non-zero-sum non-cooperative stochastic games with infinitely many interacting agents using ideas of statistical mechanics.... in the general case of asymmetric interactions, the existence of a strategy that allows any player to eliminate losses after a finite random time. In the special case of symmetric interactions ... as time goes to infinity, the game converges to a Nash equilibrium. Moreover, assuming that all agents adopt the same strategy, using arguments related to those leading to perfect simulation algorithms, spatial mixing and ergodicity are proved ... ergodicity [implies] ``fixation'', i.e. that players will adopt a constant strategy after a finite time. ... related to zero-temperature Glauber dynamics on random graphs of possibly infinite volume."]
• Pradeep Dubey and Ori Haimanko, "Learning with Perfect Information", Games and Economic Behavior 46 (2004): 304--324
• Jim Engle-Warnick, William J. McCausland and John H. Miller, "The Ghost in the Machine: Inferring Machine-Based Strategies from Observed Behavior" [i.e., inferring stochastic transducers from data; hence the inclusion here]
• Ido Erev and Alvin F. Roth, "Maximization, learning, and economic behavior", Proceedings of the National Academy of Sciences (USA) 111: 10818--10825
• Anders Eriksson and Kristian Lindgren, "A simple model of cognitive processing in repeated games", q-bio.PE/0608015
• Ignacio Esponda and Demian Pouzo, ""Berk-Nash Equilibrium: A Framework for Modeling Agents With Misspecified Models", Econometrica 84 (2016): 1093--1130
• Drew Fudenberg and David K. Levine
  • The Theory of Learning in Games
  • "Recency, consistent learning, and Nash equilibrium", Proceedings of the National Academy of Sciences (USA) 111 (2014): 10826--10829
• Douglas Gale and Hamid Sabourian, "Complexity and Competition", Econometrica 73 (2005): 739--769
• Val E. Lambson and Daniel A. Probst, "Learning by Matching Patterns", Games and Economic Behavior 46 (2004): 398--409
• Panayotis Mertikopoulos and Aris L. Moustakas, "The emergence of rational behavior in the presence of stochastic perturbations", Annals of Applied Probability 20 (2010): 1359--1388
• Liviu Panait, Karl Tuyls, Sean Luke, "Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective", Journal of Machine Learning Research 9 (2008): 423--457
• Alessandro Panella and Piotr Gmytrasiewicz, "Interactive POMDPs with finite-state models of other agents", Autonomous Agents and Multi-Agent Systems 31 (2017): 861--904 [These seem, from the abstract, like Bayesian parallels to the Foster and Young (2003) results on learning by hypothesis-testing (not cited)]
• Mark Stegeman and Paul Rhode, "Stochastic Darwinian equilibria in small and large populations", Games and Economic Behavior 49 (2004): 171--214
• Arne Traulsen, Dirk Semmann, Ralf D. Sommerfeld, Hans-Juergen Krambeck, Manfred Milinski, "Human strategy updating in evolutionary games", arxiv:1001.3768
• Finbarr Timbers, Nolan Bard, Edward Lockhart, Marc Lanctot, Martin Schmid, Neil Burch, Julian Schrittwieser, Thomas Hubert, Michael Bowling, "Approximate exploitability: Learning a best response in large games", arxiv:2004.09677
• Yevgeniy Vorobeychik, Michael P. Wellman and Satinder Singh, "Learning payoff functions in infinite games", Machine Learning 67 (2007): 145--168