CAREER:Foundational Questions in the Theory of Incentives

本文研究CAREER奖励资金的机制设计和契约理论、激励效应的经济学理论。当前的核心数学模型对于简单的环境是很好理解的，但难以扩展到更复杂的环境。该项目将开发新的基本建模工具，旨在扩大理论的范围。这些工具将使研究人员能够更好地为复杂的环境设计激励计划，包括在经济环境不确定的情况下保证业绩的激励计划。综合教育计划包括一个跨学科的会议，以连接理论家在机制设计与专家在潜在的应用领域;和推广活动与高中学生和教育工作者有兴趣在先进的数学问题解决，以建立认识这一富有成效的研究领域。这项研究促进了国家利益，因为它将指导更好的激励机制的设计。这一领域的现有理论在历史上有许多应用，包括税收、监管、采购和公司融资。新的理论将允许在这些和其他领域进一步改进和提高效率。整个项目有三个主要组成部分。(1)多维机制设计。主要的主力模型通常假设代理人的偏好是由一个一维参数概括的;将这些模型扩展到多个维度被广泛认为是具有挑战性的，但很重要。本研究将采用一种新的建模方法，基于偏好维度之间相关性的不确定性，研究有关简单机制何时以及为什么可以提供良好保证的几个问题。(2)代理人信息内生时的机制设计。在许多情况下，代理人可以采取昂贵的行动，要么获得相关信息或影响他人的信息之前，经济互动，和规划者设计的互动可能会担心，这种游戏是社会浪费。这个项目的特点机制，提供强有力的保证时，这种“信息游戏”被认为是。(3)在道德风险的情况下，当不知道代理人可以采取什么行动时，设计激励措施。本项目考虑了关于这一主题的几个理论问题，重点是了解简单的激励结构在复杂情况下何时可以最佳稳健。该研究计划还包括一些相关的研究方向，包括用简单的环境近似复杂的环境，以及激励设计师的最佳实验。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 CAREER award funds research in mechanism design and contract theory, the economic theory of the effects of incentives. Current core mathematical models are well-understood for simple environments but difficult to extend to more complicated environments. This project will develop new basic modeling tools, with the aim of broadening the scope of the theory. The tools will allow researchers to better design incentive schemes for complex environments, including incentive schemes that guarantee performance despite uncertainties about the economic environment. The integrated education plan includes an interdisciplinary conference to connect theorists in mechanism design with experts in potential application areas; and outreach activities with high school students and educators interested in advanced mathematical problem solving to build awareness of this fruitful area of study. This research promotes the national interest because it will guide the design of better incentive mechanisms. Existing theory in this area has historically had many applications, including taxation, regulation, procurement, and corporate finance. New theory will allow further improvements and better efficiency in these and other areas. The overall project has three main components. (1) Multidimensional mechanism design. Major workhorse models typically assume agents' preferences are summarized by a one-dimensional parameter; extending these models to multiple dimensions is widely viewed as challenging, yet important. This research will adapt a new modeling approach, based on uncertainty about the correlation across dimensions of preference, to study several questions concerning when and why simple mechanisms can deliver good guarantees. (2) Mechanism design when agents' information is endogenous. In many situations, agents can take costly actions to either acquire relevant information or influence others' information before an economic interaction, and a planner designing the interaction may be concerned that this gaming is socially wasteful. This project characterizes mechanisms that give robust guarantees when such "information games" are considered. (3) Design of incentives in situations of moral hazard, when it is unknown exactly what actions agents can take. This project considers several theoretical questions on this theme, focusing on understanding when simple incentive structures can be optimally robust in complex situations. The research plan also includes some related directions of study, including approximation of complex environments by simple ones, and optimal experimentation by incentive designers.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.