Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-05-18T16:47:08.522Z Has data issue: false hasContentIssue false

LEARNING IN BAYESIAN GAMES BY BOUNDED RATIONAL PLAYERS II: NONMYOPIA

Published online by Cambridge University Press:  01 June 1998

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

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.

Type
Research Article
Copyright
© 1998 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.)