The exponential convergence of Bayesian learning in normal form games

J. S. Jordan

Research output: Contribution to journalArticle

26 Scopus citations

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 - Apr 1992

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Fingerprint Dive into the research topics of 'The exponential convergence of Bayesian learning in normal form games'. Together they form a unique fingerprint.

  • Cite this