Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-14T03:23:48.978Z Has data issue: false hasContentIssue false
  • English
  • Français

LEARNING IN BAYESIAN GAMES BY BOUNDED RATIONAL PLAYERS I.

Published online by Cambridge University Press:  02 March 2005

TAESUNG KIM  and
NICHOLAS C. YANNELIS
TAESUNG KIM
Affiliation:
Seoul National University
NICHOLAS C. YANNELIS
Affiliation:
University of Illinois at Urbana–Champaign
Get access Rights & Permissions [Opens in a new window]

Abstract

We study learning in Bayesian games (or games with differential information) with an arbitrary number of bounded rational players, i.e., players who choose approximate best response strategies [approximate Bayesian Nash Equilibrium (BNE) strategies] and who also are allowed to be completely irrational in some states of the world. We show that bounded rational players by repetition can reach a limit full information BNE outcome. We also prove the converse, i.e., given a limit full information BNE outcome, we can construct a sequence of bounded rational plays that converges to the limit full information BNE outcome.

Keywords

Bayesian GameBayesian Nash EquilibriumApproximate BNEBounded Rational PlayContinuum of Players
Type
Research Article
Information
Macroeconomic Dynamics , Volume 1 , Issue 3 , September 1997 , pp. 568 - 587
Copyright
© 1997 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)