Learning to play Bayesian games

Eddie Dekel; Drew Fudenberg; David K. Levine

doi:10.1016/S0899-8256(03)00121-0

Learning to play Bayesian games

Eddie Dekel^*, Drew Fudenberg, David K. Levine

^*Corresponding author for this work

Economics

Research output: Contribution to journal › Article › peer-review

103 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 language	English (US)
Pages (from-to)	282-303
Number of pages	22
Journal	Games and Economic Behavior
Volume	46
Issue number	2
DOIs	https://doi.org/10.1016/S0899-8256(03)00121-0
State	Published - Feb 2004

Funding

This work is supported by the National Science Foundation under Grants SES-0111830, 99-86170, 97-30181, and 97-30493. We are grateful to Pierpaolo Battigalli, Dan Hojman,

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/S0899-8256(03)00121-0

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author = "Eddie Dekel and Drew Fudenberg and Levine, {David K.}",

note = "Funding Information: This work is supported by the National Science Foundation under Grants SES-0111830, 99-86170, 97-30181, and 97-30493. We are grateful to Pierpaolo Battigalli, Dan Hojman,",

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AU - Levine, David K.

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N2 - 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.

AB - 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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Learning to play Bayesian games

Abstract

Funding

ASJC Scopus subject areas

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