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Learning to Play Bayesian Games

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  • Eddie Dekel
  • Drew Fudenberg
  • David K Levine

Abstract

This paper discusses the implications of learning theory for the analysis of games with a move by Nature. One goal is to illuminate the issues that arise when modeling situations where players are learning about the distribution of Nature's move as well as learning about the opponents' strategies. A second goal is to argue that quite restrictive assumptions are necessary to justify the concept of Nash equilibrium without a common prior as a steady state of a learning process.
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Suggested Citation

  • Eddie Dekel & Drew Fudenberg & David K Levine, 2002. "Learning to Play Bayesian Games," Levine's Working Paper Archive 625018000000000151, David K. Levine.
    • Handle: RePEc:cla:levarc:625018000000000151
    as

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    File URL: http://www.dklevine.com/papers/bg_July22_2002.pdf
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    References listed on IDEAS

    as
    1. Rubinstein Ariel & Wolinsky Asher, 1994. "Rationalizable Conjectural Equilibrium: Between Nash and Rationalizability," Games and Economic Behavior, Elsevier, vol. 6(2), pages 299-311, March.
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