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LEARNING IN BAYESIAN GAMES BY BOUNDED RATIONAL PLAYERS I.

Published online by Cambridge University Press:  02 March 2005

TAESUNG KIM
Affiliation:
Seoul National University
NICHOLAS C. YANNELIS
Affiliation:
University of Illinois at Urbana–Champaign

Abstract

We study learning in Bayesian games (or games with differentialinformation) 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 alsoare allowed to be completely irrational in some states of the world.We show that bounded rational players by repetition can reach a limitfull information BNE outcome. We also prove the converse, i.e., givena limit full information BNE outcome, we can construct a sequence ofbounded rational plays that converges to the limit full informationBNE outcome.

Information

Type
Research Article
Copyright
© 1997 Cambridge University Press

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