Learning to play Bayesian games

Eddie Dekel*, Drew Fudenberg, David K. Levine

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)282-303
Number of pages22
JournalGames and Economic Behavior
Volume46
Issue number2
DOIs
StatePublished - Feb 2004

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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