A Probabilistic Model of Learning in Games
提出一个概率模型，研究重复互动如何可能产生战略意图的共同知识，并证明在多种博弈中，最终行为几乎必然收敛于纳什均衡。
This paper presents a new, probabilistic model of learning in games which investigates the often stated intuition that common knowledge of strategic intent may arise from repeated interaction. 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, including those with strategic complementarities, potential functions, and bandwagon effects, all such sets are singleton Nash.