Bayesian learning in repeated games of incomplete information

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