CAREER: Towards a Robust Theory of Mechanism Design

赞助搜索、在线市场、众包和共享经济等不断增长的技术平台正在成为现代经济的基石。这些在线市场和平台面临的一个中心问题是如何设计正确的激励结构，使只对优化自己的公用事业感兴趣的参与者有动力采取有助于实现设计者目标的行动。机构设计的经典经济学理论致力于解决这个问题。然而，由于严重依赖分配假设、侧重于最佳但不切实际的机制以及不重视信息的作用等几个缺点，为实践提供具体的指导方针还不够有力或现实。本研究的目标是通过提供新的框架和解决方案来缓解和解决这些缺陷，从而构建一个健壮的机制设计理论。在解决这些弱点的过程中，这个项目不仅提高了机制设计的实用性，还发展了一些见解，以回答一些长期存在的理论悬而未决的问题。该项目包括一项教育计划，其中包括研究生和本科课程的课程开发以及研究生的培训和本科生的研究机会。更具体地说，本项目主要围绕以下三个方面展开研究。(1)弱化贝叶斯假设：机制设计中一个常见但不切实际的假设是，参与者的偏好是从已知分布中得出的。调查者计划在两种替代和现实的分布访问模型下重新审查机制设计：(A)对分布的样本访问和(B)最大-最小稳健学习--仅给定近似分布，学习在未知真实分布下表现良好的机制。(2)理解简单性和最优化之间的权衡：最优机制通常过于复杂，不切实际。调查员将开发一个框架，以设计简单和近似最优的机制，并解决多项目拍卖和双边市场中的公开问题。(Iii)算法视角的信息设计：与传统的机制设计不同，传统的机制设计侧重于通过直接激励来影响参与者，信息设计研究信息披露如何改变参与者的行为。研究人员旨在通过算法镜头了解信息结构的设计，重点关注最优信息披露方案的计算复杂性。这项工作将依赖于来自优化、学习理论、统计学和机器学习以及统计物理的工具，并在这些领域和机制设计之间建立新的联系。这个奖项反映了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。

The ever-growing technology platforms such as sponsored search, online marketplaces, crowdsourcing, and sharing economies are becoming cornerstones of the modern economy. A central problem faced by these online markets and platforms is how to design the right incentive structure, so that the participants, who are only interested in optimizing their own utilities, are motivated to take actions that will help to realize the designer's goals. The classic economic theory of mechanism design is dedicated to addressing this problem. However, it is not yet robust or realistic enough to provide concrete guidelines for practice due to several shortcomings such as the strong reliance on distributional assumptions, the focus on optimal but impractical mechanisms, as well as the lack of attention to the role of information. The goal of this research is to build a robust mechanism design theory by offering new frameworks and solutions to alleviate and resolve these shortcomings. In tackling these weaknesses, this project not only improves the practicality of mechanism design, but also develops insights to answer some of the long-standing theoretical open questions. This project includes an education plan that incorporates course development of both graduate and undergraduate courses as well as training for graduate students and research opportunities for undergraduates. More concretely, this project focuses on the following three research thrusts. (i) Weakening Bayesian assumptions: a common but unrealistic assumption in mechanism design is that the participants' preferences are drawn from a known distribution. The investigator plans to revisit mechanism design under two alternative and realistic distribution access models: (a) sample access to the distribution and (b) max-min robust learning -- given only an approximate distribution, learn a mechanism that performs well under the unknown true distribution. (ii) Understanding the tradeoff between simplicity and optimality: the optimal mechanism is usually too complex to be practical. The investigator will develop a framework to design simple and approximately optimal mechanisms and address open questions in multi-item auctions and two-sided markets. (iii) Information design via an algorithmic lens: unlike traditional mechanism design, which focuses on influencing participants via direct incentives, information design studies how information revelation can shift the participants’ behavior. The investigator aims to understand the design of information structure via an algorithmic lens, focusing on the computational complexity of the optimal information revelation scheme. This work will rely on tools from optimization, learning theory, statistics and machine learning, and statistical physics, and forge new connections between these fields and mechanism design.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.