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dc.contributor.authorDekel, Eddie
dc.contributor.authorFudenberg, Drew
dc.contributor.authorLevine, David
dc.date.accessioned2009-07-28T20:53:34Z
dc.date.issued2004
dc.identifier.citationDekel, Eddie, Drew Fudenberg, and David K. Levine. 2004. Learning to play Bayesian games. Games and Economic Behavior 46, no. 2: 282-303.en
dc.identifier.issn0899-8256en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:3200612
dc.description.abstractThis 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.en
dc.description.sponsorshipEconomicsen
dc.language.isoen_USen
dc.publisherElsevieren
dc.relation.isversionofhttp://dx.doi.org/10.1016/S0899-8256(03)00121-0en
dash.licenseLAA
dc.titleLearning to Play Bayesian Gamesen
dc.relation.journalGames and Economic Behavioren
dash.depositing.authorFudenberg, Drew
dc.identifier.doi10.1016/S0899-8256(03)00121-0*
dash.contributor.affiliatedFudenberg, Drew


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