Journal article
LEARNING IN BAYESIAN GAMES BY BOUNDED RATIONAL PLAYERS II: NONMYOPIA
Macroeconomic dynamics, v 2(2), pp 141-155
01 Jun 1998
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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.
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Details
- Title
- LEARNING IN BAYESIAN GAMES BY BOUNDED RATIONAL PLAYERS II: NONMYOPIA
- Creators
- Konstantinos Serfes - University of Illinois Urbana-ChampaignNicholas C. Yannelis - University of Illinois Urbana-Champaign
- Publication Details
- Macroeconomic dynamics, v 2(2), pp 141-155
- Publisher
- Cambridge University Press
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Economics (School of Economics)
- Web of Science ID
- WOS:000074405000001
- Scopus ID
- 2-s2.0-0032222054
- Other Identifier
- 991021867239004721
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- Web of Science research areas
- Economics