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

协作研究：机制设计和不完全信息博弈的新信息鲁棒方法

• 批准号: 2215475
• 负责人: Benjamin Brooks
• 金额: $ 29.44万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215475&HistoricalAwards=false
• 关键词: Collaborative; Research; New; Informationally; Robust

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.

该项目开发了新的工具，用于在战略游戏中进行稳健的预测，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已实现的状态，但他们可能会收到允许更新每种可能状态的可能性的信号）。然而，在许多实际情况下，经典方法是研究参与者信息的固定模型下的行为。 ，分析师或政策制定者可能担心参与者信息的错误指定。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。首先，我们开发了三个组件。一种新的解决方案概念，描述了有关收益相关状态的常见先验信念下的合理化行为，这是称为贝叶斯相关均衡（BCE）的解决方案概念的概括，用较弱的临时相关合理性概念取代了通常的纳什均衡。重要的，因为在许多情况下（例如很少发生的频谱拍卖），战略代理人可能没有足够的时间或经验来收敛纳什均衡；尽管如此，我们可以基于行动是理性的前提对行为做出重要的预测。其次，我们研究了 BCE 的独特概括，其中玩家信息存在上限，这补充了 BCE 现有公式中的信息下限；获得在代理人无法访问所有与收益相关的信息（例如银行挤兑或“有毒”资产拍卖）的情况下进行更现实的预测第三，我们将新兴的信息鲁棒性最优机制理论扩展到并应用到问题。设计有效的交易机制。现有的双边贸易模型主要关注简单且程式化的情况，其中买方和卖方都知道各自的价值观，并且这些价值观是紧密独立的，使用信息稳健的方法，我们推导出新的交易机制。有保证的即使买家和卖家的价值相互依赖且信息相互关联，我们也可以利用我们对强大交易平台的见解来提高存在信息摩擦的各种市场的效率，从而从贸易中产生不小的收益。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。