Collaborative Research: Evidence in Economic Models

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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.