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A General Approach to Rational Learning in Games

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  • Gilli, Mario

Abstract

This paper provides a general framework for analysing rational learning in strategic situations in which the players have private priors and private information. The author analyses the behaviour of Bayesian rational players both in a repeated game and in a recurrent game when they are uncertain about opponents' behaviour and the game they are playing. The aim of the paper is to explain how Bayesian rational agents learn by playing and to characterize the outcome of this learning process. By studying the concept of "conjectural equilibrium" and analysing the process of convergence of players' behaviour, the roles played by the notions of merging and of consistency are demonstrated. Copyright 2001 by Blackwell Publishing Ltd and the Board of Trustees of the Bulletin of Economic Research

Suggested Citation

  • Gilli, Mario, 2001. "A General Approach to Rational Learning in Games," Bulletin of Economic Research, Wiley Blackwell, vol. 53(4), pages 275-303, October.
    • Handle: RePEc:bla:buecrs:v:53:y:2001:i:4:p:275-303
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    Cited by:

    1. Takako Fujiwara-Greve & Carsten Krabbe Nielsen, 2021. "Algorithms may not learn to play a unique Nash equilibrium," Journal of Computational Social Science, Springer, vol. 4(2), pages 839-850, November.
    2. Levy, Yehuda John, 2015. "Limits to rational learning," Journal of Economic Theory, Elsevier, vol. 160(C), pages 1-23.
    3. Mario Gilli, 2002. "Rational Learning in Imperfect Monitoring Games," Working Papers 46, University of Milano-Bicocca, Department of Economics, revised Mar 2002.

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