Bayesian learning in repeated games of incomplete information
In Nachbar  and, more definitively, Nachbar , 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.
Volume (Year): 18 (2001)
Issue (Month): 2 ()
|Note:||Received: 15 October 1997/Accepted: 17 March 1999|
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