Efficiency in Games With Markovian Private Information
研究了玩家私人收益按独立马尔可夫链演化的重复贝叶斯博弈，证明在折现因子趋近1时，任何高于平稳最小最大值的帕累托有效收益向量都能被完美贝叶斯均衡近似实现。
We study repeated Bayesian games with communication and observable actions in which the players' privately known payoffs evolve according to an irreducible Markov chain whose transitions are independent across players. Our main result implies that, generically, any Pareto-efficient payoff vector above a stationary minmax value can be approximated arbitrarily closely in a perfect Bayesian equilibrium as the discount factor goes to 1. As an intermediate step, we construct an approximately efficient dynamic mechanism for long finite horizons without assuming transferable utility.