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LEARNING IN BAYESIAN GAMES BY BOUNDED RATIONAL PLAYERS II: NONMYOPIA

Published online by Cambridge University Press:  01 June 1998

Konstantinos Serfes  and
Nicholas C. Yannelis
Konstantinos Serfes
Affiliation:
University of Illinois at Urbana–Champaign
Nicholas C. Yannelis
Affiliation:
University of Illinois at Urbana–Champaign
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Abstract

We generalize results of earlier work on learning in Bayesian games by allowing players to make decisions in a nonmyopic fashion. In particular, we address the issue of nonmyopic Bayesian learning with an arbitrary number of bounded rational players, i.e., players who choose approximate best-response strategies for the entire horizon (rather than the current period). We show that, by repetition, nonmyopic bounded rational players can reach a limit full-information nonmyopic Bayesian Nash equilibrium (NBNE) strategy. The converse is also proved: Given a limit full-information NBNE strategy, one can find a sequence of nonmyopic bounded rational plays that converges to that strategy.

Keywords

Bayesian GameNash EquilibriumNonmyopiaBayesian LearningBounded Rationality
Type
Research Article
Information
Macroeconomic Dynamics , Volume 2 , Issue 2 , June 1998 , pp. 141 - 155
Copyright
© 1998 Cambridge University Press

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