Collaborative Research: New Informationally Robust Approaches to Mechanism Design and Games of Incomplete Information

该项目开发了新的工具，用于战略游戏的稳健预测，其中结果取决于一些未观察到的自然状态（尽管玩家没有观察到实现的状态，但他们可能会接收到允许更新每种可能状态可能性的信号）。这些博弈是经济学理论中的基本工具，用于理解各种情况下的行为，如寡头垄断、拍卖、选举竞争和银行挤兑。经典的方法是在玩家信息的固定模型下研究行为。然而，在许多实际情况下，分析师或政策制定者可能会担心参与者信息的错误描述。我们开发的工具可以预测参与者的行为，并提供有关市场设计的政策建议，而不依赖于信息环境的完整规范。该项目有三个组成部分。首先，我们提出了一个新的解概念，描述了在关于支付相关状态的共同先验信念下的可合理化行为。这是一个被称为贝叶斯相关均衡（BCE）的解决概念的推广，用一个较弱的临时相关合理化概念取代了通常的纳什均衡。这一点很重要，因为在许多情况下（如频谱拍卖不经常发生），战略代理可能没有足够的时间或经验来收敛于纳什均衡；然而，我们可以基于在某些（不一定是正确的）信念下行为是理性的这一前提，对行为做出重要的预测。其次，我们研究了BCE的一个独特推广，其中存在玩家信息的上界。这补充了现有BCE公式中信息的下限；在代理人无法获得所有与收益相关的信息（如银行挤兑或“有毒”资产的拍卖）的情况下，它使我们能够获得更现实的预测。第三，我们将新兴的信息稳健最优机制理论扩展并应用于有效交易机制的设计问题。现有的双边贸易模型主要集中在简单和程式化的情况下，买卖双方都知道各自的价值，这些价值在统计上是独立的。使用信息稳健的方法，我们推导出新的交易机制，即使在买方和卖方的价值是相互依赖的，信息是相关的情况下，也能保证从贸易中产生可观的收益。我们对稳健交易平台的见解可以用于提高各种存在信息摩擦的市场的效率，例如金融证券或健康保险市场。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project develops new tools for robust predictions in strategic games where the outcome depends on some unobserved state of nature (although the players do not observe the realized state, they may receive signals allowing to update the likelihood of each possible state). These games are a fundamental tool in economic theory for understanding behavior in a wide range of settings, such as oligopoly, auctions, electoral competition, and bank runs. The classical approach is to study behavior under a fixed model of player’s information. In many practical settings, however, an analyst or policy maker may be concerned about misspecification of the players' information. The tools that we develop give predictions about the players' behavior and policy recommendations about market design, without relying on a full specification of the informational environment.The project has three components. First, we develop a new solution concept that characterizes rationalizable behavior under common prior beliefs about payoff relevant states. This is a generalization of the solution concept known as Bayes’ correlated equilibrium (BCE), replacing the usual Nash equilibrium with a weaker notion of interim correlated rationalizability. This is important, because in many settings (such as spectrum auctions which occur infrequently), strategic agents may not have had sufficient time or experience to converge on a Nash equilibrium; nevertheless, we can make non-trivial predictions about behavior based on the premise that actions are rational under some (not necessarily correct) beliefs. Second, we study a distinct generalization of BCE in which there is an upper bound on players’ information. This complements the lower bounds on information found in the existing formulations of BCE; and it allows us to obtain more realistic predictions in settings where agents do not have access to all payoff-relevant information (such as bank runs or auctions of "toxic" assets). Third, we extend and apply the burgeoning theory of informationally-robust optimal mechanisms to the problem of designing efficient trading mechanisms. Existing models of bilateral trade are primarily focused on the simple and stylized case where buyer and seller each know their respective values, and those values are statistically independent. Using the informationally-robust approach, we derive new trading mechanisms that are guaranteed to produce non-trivial gains from trade even when buyer and seller values are interdependent and information is correlated. The insights we develop about robust trading platforms could be utilized to improve efficiency in a wide variety of markets with informational frictions, such as the markets for financial securities or health insurance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.