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Learning and Equilibrium

Author

Listed:
  • 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.

Suggested Citation

  • Drew Fudenberg & David K. Levine, 2009. "Learning and Equilibrium," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 385-420, May.
  • Handle: RePEc:anr:reveco:v:1:y:2009:p:385-420
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Dietrichson, Jens & Jochem, Torsten, 2014. "Organizational Coordination and Costly Communication with Boundedly Rational Agents," Working Papers 2014:2, Lund University, Department of Economics.
    2. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
    3. Liu, Zhen, 2016. "Games with incomplete information when players are partially aware of others’ signals," Journal of Mathematical Economics, Elsevier, vol. 65(C), pages 58-70.
    4. 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.
    5. Topi Miettinen, 2012. "Paying attention to payoffs in analogy-based learning," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 50(1), pages 193-222, May.
    6. 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.
    7. Ignacio Esponda & Demian Pouzo, 2014. "Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models," Papers 1411.1152, arXiv.org, revised May 2016.
    8. Oyarzun, Carlos, 2014. "A note on absolutely expedient learning rules," Journal of Economic Theory, Elsevier, vol. 153(C), pages 213-223.
    9. 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.
    10. van Damme, E.E.C., 2015. "Game theory : Noncooperative games," Other publications TiSEM ff518f2b-501f-4d99-817b-c, Tilburg University, School of Economics and Management.
    11. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
    12. Zhijian Wang & Bin Xu, 2014. "Cycling in stochastic general equilibrium," Papers 1410.8432, arXiv.org.

    More about this item

    Keywords

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

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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