Learning to Play Bayesian Games

Eddie Dekel, Drew Fudenberg and David Levine

Scholarly Articles from Harvard University Department of Economics

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.

Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (104)

Published in Games and Economic Behavior

Downloads: (external link)
http://dash.harvard.edu/bitstream/handle/1/3200612/fudenberg_Bayesiangames.pdf (application/pdf)

Related works:
Journal Article: Learning to play Bayesian games (2004)
Working Paper: Learning to Play Bayesian Games (2002)
Working Paper: Learning to Play Bayesian Games (2001)
Working Paper: Learning to Play Bayesian Games (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hrv:faseco:3200612

Access Statistics for this paper

More papers in Scholarly Articles from Harvard University Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Office for Scholarly Communication ().