Learning efficient equilibria in repeated games
提出一种随机学习规则，能在无限重复博弈中选出子博弈完美且收益有效的均衡，具体收益取决于玩家的实验方式，分别对应Kalai-Smorodinsky和最大最小讨价还价解。
The folk theorem tells us that a wide range of payoffs can be sustained as equilibria in an infinitely repeated game. Existing results about learning in repeated games suggest that players may converge to an equilibrium, but do not address selection between equilibria. I propose a stochastic learning rule that selects a subgame-perfect equilibrium of the repeated game in which payoffs are efficient. The exact payoffs selected depend on how players experiment; two natural specifications yield the Kalai–Smorodinsky and maxmin bargaining solutions, respectively.