The exponential convergence of Bayesian learning in normal form games

James Schuyler Jordan

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

This paper continues the study of Bayesian learning processes for general finite-player, finite-strategy normal form games. Bayesian learning was introduced in an earlier paper by the present author as an iterative mechanism by which players can learn Nash equilibria. The main result of the present paper is that if prior beliefs are sufficiently uniform and expectations converge to a "regular" Nash equilibrium, then the rate of convergence is exponential.

Original languageEnglish (US)
Pages (from-to)202-217
Number of pages16
JournalGames and Economic Behavior
Volume4
Issue number2
DOIs
StatePublished - Jan 1 1992

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Nash equilibrium
Bayesian learning
Normal form games
Learning process
Rate of convergence

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Finance

Cite this

Jordan, James Schuyler. / The exponential convergence of Bayesian learning in normal form games. In: Games and Economic Behavior. 1992 ; Vol. 4, No. 2. pp. 202-217.
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The exponential convergence of Bayesian learning in normal form games. / Jordan, James Schuyler.

In: Games and Economic Behavior, Vol. 4, No. 2, 01.01.1992, p. 202-217.

Research output: Contribution to journalArticle

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