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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
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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

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