A Probabilistic Model of Learning in Games.

This paper presents a new, probabilistic model of learning in games. The model is set in the usual repeated game framework but the two key assumptions are framed in terms of the likelihood of beliefs and actions conditional on the history of play. The first assumption formalizes the basic intuition of the learning approach; the second, the indeterminacy that inspired resort to learning models in the first place. Together the assumptions imply that, almost surely, play will remain almost always within one of the stage game's 'minimal inclusive sets.' In important classes of games, all such sets are singleton Nash. Copyright 1996 by The Econometric Society.