Collaborative Research: Evidence in Economic Models

该奖项用于经济理论方面的研究。该小组将研究如何获得和使用确凿的信息或证据，使感兴趣的各方能够证明他们希望提出的一些主张。什么时候才能设计出这样一种社会机构，以便向有关各方提供足够强大的激励，以获得足够可信的证据，从而实现有效的结果？当效率是不可能的时候，最好的机构可能产生的效率扭曲的本质是什么？研究小组将开发研究这些问题的数学模型，包括使分析足够容易处理以产生清晰结果的工具。更广泛地说，这项工作将促进在这一大类有趣的环境中研究应用经济模型。通过提供使分析更易于处理的工具，这项研究可能会产生新的经济机构类别，并对现有的机构提供新的认识。这些工具还将有助于研究企业和政府等组织的结构。该项目的重点是当代理人可以获得噪音证据时的机制设计。一般模型与广泛的“硬信息”经济模型有关，例如贝叶斯说服和检验模型。该团队将开发工具来简化机构设计问题，以便能够专注于更易处理的机构类别。特别是，他们将开发条件，在这种条件下，人们可以识别委托人将要求提供的证据，以及/或委托人将建议的证据生成行动，以提供任何类型的报告。无论是一起还是分开，这种识别都将极大地简化最佳机制的表征。调查人员还会将最优机制与委托人无法承诺的结果进行比较。最后，他们将描述可能实现有效结果的环境以及无法实现效率时扭曲的性质。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。

This award funds research in economic theory. The team will study the acquisition and use of hard information or evidence which enables interested parties to prove some assertions they wish to make. When can social institutions be designed in such a way as to provide strong enough incentives to interested parties to acquire enough sufficiently credible evidence as to enable efficient outcomes? When efficiency is not possible, what is the nature of the distortions from efficiency generated by the best institutions that are possible? The research team will develop mathematical models for studying these questions, including tools for making the analysis sufficiently tractable to yield clear results. More broadly, the work will facilitate research of applied economic models in this large class of interesting environments. By providing tools for making the analysis more tractable, the research may generate new classes of economic institutions and shed new light on existing ones. The tools will also be useful in studying the structure of organizations, such as firms and governments. The project focuses on mechanism design when agents can acquire noisy evidence. The general model has connections to a wide range of economic models of "hard information", such as Bayesian persuasion and models of testing. The team will develop tools for simplifying the mechanism design problem to be able to focus on a more tractable class of mechanisms. In particular, they will develop conditions under which one can identify the evidence the principal will request given any claim by the agent and/or the evidence generation actions the principal will recommend given any type report. Together or separately, this identification will greatly simplify characterization of the optimal mechanism. The investigators will also compare optimal mechanisms to outcomes when the principal cannot commit himself. Finally, they will characterize environments in which efficient outcomes are possible and the nature of distortions when efficiency is not achievable.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.