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- Title
Bayesian learning in repeated games of incomplete information.
- Authors
Nachbar, John H.
- Abstract
Abstract. In Nachbar [20] and, more definitively, Nachbar [22], I argued that, for a large class of discounted infinitely repeated games of complete information (i.e. stage game payoff functions are common knowledge), it is impossible to construct a Bayesian learning theory in which player beliefs are simultaneously weakly cautious, symmetric, and consistent. The present paper establishes a similar impossibility theorem for repeated games of incomplete information, that is, for repeated games in which stage game payoff functions are private information. Received: 15 October 1997/Accepted: 17 March 1999
- Subjects
BAYESIAN analysis; INFORMATION theory; GAME theory
- Publication
Social Choice & Welfare, 2001, Vol 18, Issue 2, p303
- ISSN
0176-1714
- Publication type
Article
- DOI
10.1007/PL00007181