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Learning and Equilibrium

Author

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  • Levine, David K.
  • Fudenberg, Drew

Abstract

The theory of learning in games studies how, which and what kind of equilibria might arise as a consequence of a long-run non-equilibrium 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 (as in extensive-form games) players do not observe the strategies chosen by their opponents, 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 due to cognitive limitations and systematic inferential errors.

Suggested Citation

  • Levine, David K. & Fudenberg, Drew, 2009. "Learning and Equilibrium," Scholarly Articles 4382413, Harvard University Department of Economics.
  • Handle: RePEc:hrv:faseco:4382413
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    Citations

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    Cited by:

    1. 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.
    2. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
    3. Dietrichson, Jens, 2013. "Coordination Incentives, Performance Measurement and Resource Allocation in Public Sector Organizations," Working Papers 2013:26, Lund University, Department of Economics.
    4. Zhijian Wang & Bin Xu, 2014. "Cycling in stochastic general equilibrium," Papers 1410.8432, arXiv.org.
    5. Phillip Johnson & David K Levine & Wolfgang Pesendorfer, 1998. "Evolution and Information in a Prisoner's Dilemma Game," Levine's Working Paper Archive 2138, David K. Levine.
    6. Dietrichson, Jens & Gudmundsson, Jens & Jochem, Torsten, 2014. "Let's Talk It Over: Communication and Coordination in Teams," Working Papers 2014:2, Lund University, Department of Economics, revised 18 Apr 2018.
    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. 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.
    9. 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.
    10. Oyarzun, Carlos, 2014. "A note on absolutely expedient learning rules," Journal of Economic Theory, Elsevier, vol. 153(C), pages 213-223.
    11. 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.
    12. 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.
    13. Dietrichson, Jens & Jochem, Torsten, 2014. "Organizational coordination and costly communication with boundedly rational agents," Comparative Institutional Analysis Working Paper Series 2014:1, Lund University, Comparative Institutional Analysis, School of Economics and Management.

    More about this item

    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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