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Learning and Equilibrium

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  • Drew Fudenberg
  • David K. Levine

    ()
    (Department of Economics, Harvard University, Cambridge, Massachusetts
    Department of Economics, Washington University of St. Louis, St. Louis, Missouri)

Abstract

The theory of learning in games explores how, which, and what kind of equilibria might arise as a consequence of a long-run nonequilibrium process of learning, adaptation, and/or imitation. If agents’ strategies are completely observed at the end of each round (and agents are randomly matched with a series of anonymous opponents), fairly simple rules perform well in terms of the agent’s worst-case payoffs, and also guarantee that any steady state of the system must correspond to an equilibrium. If players do not observe the strategies chosen by their opponents (as in extensive-form games), then learning is consistent with steady states that are not Nash equilibria because players can maintain incorrect beliefs about off-path play. Beliefs can also be incorrect because of cognitive limitations and systematic inferential errors.

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Bibliographic Info

Article provided by Annual Reviews in its journal Annual Review of Economics.

Volume (Year): 1 (2009)
Issue (Month): 1 (05)
Pages: 385-420

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Handle: RePEc:anr:reveco:v:1:y:2009:p:385-420

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Related research

Keywords: nonequilibrium dynamics; bounded rationality; Nash equilibrium; self-confirming equilibrium;

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Cited by:
  1. Phillip Johnson & David K. Levine & Wolfgang Pesendorfer, 1998. "Evolution and Information in a Prisoner's Dilemma Game," Working Papers 9805, Centro de Investigacion Economica, ITAM.
  2. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
  3. Dietrichson, Jens & Jochem, Torsten, 2014. "Organizational coordination and costly communication with boundedly rational agents," Comparative Institutional Analysis Working Paper Series 2014:1, Comparative Institutional Analysis, Lund University School of Economics and Management.
  4. Topi Miettinen, 2012. "Paying attention to payoffs in analogy-based learning," Economic Theory, Springer, vol. 50(1), pages 193-222, May.
  5. Cui, Zhiwei & Zhai, Jian, 2010. "Escape dynamics and equilibria selection by iterative cycle decomposition," Journal of Mathematical Economics, Elsevier, vol. 46(6), pages 1015-1029, November.
  6. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
  7. Clark Bowman & Jonathan Hodge & Ada Yu, 2014. "The potential of iterative voting to solve the separability problem in referendum elections," Theory and Decision, Springer, vol. 77(1), pages 111-124, June.

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