Collaborative Research: Sequentially Optimal Mechanism Design

该奖项资助机制设计经济学理论的研究。这一经济学领域侧重于理解激励如何影响经济结果，目标是设计有效和高效的支付和奖励计划。该项目旨在通过提供一种新的工具来研究当长期承诺不现实时所产生的问题，从而扩展机制设计理论。例如，在线零售商、保险公司和银行随着时间的推移与他们的客户进行互动。随着这些公司了解他们的客户，他们可能想通过向一些客户提供个性化的服务来改变合同条款。同样，政府可能会就主权债务签署协议，但救助和重新谈判可能会改变原始协议的条款。这种新方法有望为这一重要的经济理论领域带来新的应用，包括债务和抵押合同、货币政策、在线平台设计、在线隐私和广告销售拍卖等一系列应用。因此，这项研究可以为企业和政府决策者提供更有效的管理方法。由于缺乏一种易于处理的方法，在有限承诺下的最佳机制设计方面的进展受到阻碍。该团队将开发一种新的方法，该方法基于这样一种思想，即一种机制不仅应该编码确定分配的规则，还应该编码设计人员从交互中获得的信息。这意味着设计师学习多少内容将成为设计的重要组成部分。该项目有三个关键部分。首先，团队将开发这个新工具，这将类似于在经典机制设计中使用的启示原理。其次，该团队将使用该方法来描述无限视界设置下的最优交易机制。这种表征为科斯猜想提供了坚实的基础，求解方法可以作为在无限视界环境中描述政府承诺能力有限时的最优财政政策或社会保险的原型。第三，该团队将检查信息收集政策的最佳设计（例如，cookie的使用）以及公司收集消费者信息时产生的透明度和隐私问题。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 award funds research in the economic theory of mechanism design. This area of economics focuses on understanding how incentives affect economic outcomes, with the goal of designing effective and efficient payment and reward plans. The project aims to expand mechanism design theory by providing a new tool to study the problems caused when long-term commitment to a contract is unrealistic. For example, online retailers, insurance companies, and banks interact with their customers over time. As these firms learn about their customers, they may want to change contract terms by making personalized offers to some customers. In the same way, governments may sign agreements about sovereign debt, but bailouts and renegotiation may change the terms of the original agreement. The new method has promise for new applications of this important area of economic theory, including a range of applications from debt and mortgage contracts, monetary policy, the design of online platforms, online privacy, and auctions for advertising sales. As a result, the research could result in more effective management methods for businesses and government policy makers.Progress on optimal mechanism design under limited commitment has been hindered by a lack of a tractable methodology. The team will develop a new methodology based on the idea that a mechanism should encode not only the rules to determine the allocation, but also the information the designer obtains from the interaction. This means that how much the designer learns becomes an explicit part of the design. The project has three key parts. First, the team will develop this new tool, which will be akin to the use of the revelation principle in classical mechanism design. Second, the team will use the method to characterize optimal trading mechanisms in infinite horizon settings. The characterization provides a solid foundation to Coase's conjecture and the solution method can serve as a prototype to characterize optimal fiscal policy or social insurance when governments have limited commitment ability in infinite horizon settings. Third, the team will examine the optimal design of information collection policies (for example, the use of cookies) and the resulting issues of transparency and privacy that arise when firms collect information about consumers.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.