Learning in Games

Last update: 21 Apr 2025 21:17
First version: 18 August 2005

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

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

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]

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