Designing Efficient Mechanisms for Dynamic Bilateral Trading Games
研究无限期博弈中每期分配一件商品给两个玩家的问题，玩家每期的估值是私人信息且随时间变化。分析了估值动态的两种特殊情况（序列相关和边干边学），并探讨存在高效、贝叶斯激励相容、个体理性且预算平衡机制的条件。
This paper studies the problem of allocating a good between two players in each period of an infinite-horizon game. The players' valuations in each period are private information, and the valuations change over time. We analyze two special cases for the dynamics of valuations: correlated where players' valuations are exogenous but serially correlated; and learning by doing, where a player's past consumption improves his current distribution of valuations, but his valuations are otherwise uncorrelated. We analyze conditions under which there exists an efficient, Bayesian incentive-compatible (BIC), individually rational (IR), budget-balanced (BB) mechanism, when the mechanism designer has commitment power. We consider