Fraud-proof Mechanism Design

这项研究的重点是在代理人可能从事代价高昂(或高风险)的欺诈的情况下，设计更好的合同和拍卖机制。该项目扩展了机械设计的理论(这通常被认为是经济学的工程方面)。具体地说，这项工作提高了我们对公共住房单位、选择性学校席位、人体器官和其他物品的分配的理解，这些物品的分配不是通过货币转移完成的，可能会受到伪造申请的影响。资格通常基于一个分数(度量)，该分数代表了将单位分配给个人的社会价值。然而，对该指标的依赖会产生强烈的动机来玩弄它。因此，伪造、洗白、为考试授课或操纵统计数据等做法司空见惯。臭名昭著的例子包括医生升级患者的治疗以提高他们在器官等待名单上的优先顺序，家庭使用虚假地址进入理想的公立学校，以及富裕的父母在大学招生丑闻期间帮助他们的孩子进入美国高度挑剔的大学的各种精心设计的方式。玩弄制度会导致分配不当、结果不公平，并侵蚀公众信任。更擅长游戏的社会经济群体可以更快地获得器官移植，为他们的孩子提供更好的学校作业，等等。错误的分配不仅不公平，而且在某些情况下还可能造成生命损失。这位研究人员开发了在防止欺诈的同时最大化配置效率的程序，这意味着个人无法从博弈得分中受益。与大多数机制设计文献假设误报是没有成本的不同，由该奖项资助的研究以代理人的自然得分为特征，他们可以在付出代价的情况下伪造得分，并通过其他私人已知的维度(品味、游戏能力)来表征代理人，他们可以免费虚报。分析表明，人们如何利用伪造成本来设计最优分配程序。发展的解决方案方法论是对Myerson(1981)提出的技术的补充，Myerson(1981)的技术已被广泛应用于迁移环境，以及Amado等人使用的拉格朗日技术。(2006)和其他没有转让的设置。该研究项目提出了一种解决机制设计问题的开创性方法，无需转移和代价高昂的欺诈，突出了与最优运输理论文献的新联系。在经济学中，它扩展了主要的机制设计范式，允许代理人的私人信息具有“硬”(错误报告代价高昂)和“软”(自由错误报告)两个维度。我们提供的背景和见解可以应用于联邦资金分配所依据的评级系统的设计(例如，医疗补助提供者和养老院的评级系统)。它们也可以应用于会计和税收准则的设计。所有这些系统经常受到游戏和操纵的困扰，这使得我们基于代价高昂的类型伪造的建模变得相关。该项目还对计算机科学产生了跨学科的影响。计算机科学理论与算法操作有关。考虑到这样的操作可能代价高昂，并且算法的抽象建模类似于没有转移的机构，建模和解决方法可以应用于防操纵算法的设计。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 research focuses on designing better contracts and auction mechanisms in contexts where agents can engage in costly (or risky) fraud. The project extends the theory of mechanism design (which is often considered as the engineering side of economics.). Specifically, this work improves our understanding of the allocation of public housing units, seats in selective schools, human organs, and other goods whose assignments are done without monetary transfers and may be affected by falsified applications. Eligibility is usually based on a score (metric) that proxies the social value of assigning a unit to an individual. However, reliance on the metric creates strong incentives to game it. As a result, practices such as forgery, greenwashing, teaching to the test, or manipulating statistics are commonplace. Infamous examples include doctors escalating their patients' treatments to increase their priority on organ waiting lists, families using a fake address to gain access to a desirable public school, and the various elaborate ways well-off parents facilitated admissions of their children to highly selective universities in the US during the college admissions scandal. Gaming the system leads to misallocation, unfair outcomes, and erodes public trust. Socio-economic groups better at gaming achieve faster access to organ transplants, better school assignments for their children, and so forth. The misallocations are not only unfair but they can also cost lives in some cases. The researcher develops procedures that maximize allocative efficiency while being fraud-proof, meaning individuals cannot benefit from gaming their scores.In contrast to the majority of the mechanism design literature, which assumes that misreporting is costless, the research funded by this award characterizes agents by their natural score, which they can falsify at a cost, and by other privately-known dimensions (tastes, gaming abilities) that they can misrepresent at no cost. The analysis shows how one can leverage falsification costs to design optimal allocation procedures. The developed solution methodology complements techniques stemming from Myerson (1981), which have been widely applied to settings with transfers, as well as the Lagrangian techniques employed by Amador et al. (2006) and others for settings without transfers. The research program suggests a pioneering way of tackling problems of mechanism design without transfers and costly fraud, highlighting novel connections to the literature of optimal transportation theory. Within economics, it extends the main mechanism design paradigm by allowing agents' private information to have 'hard' (costly to misreport) and 'soft' (free to misreport) dimensions. The setting and insights we provide can be applied to the design of rating systems based on which federal funds are allocated (for example, rating systems for Medicaid providers and nursing homes). They can also be applied to the design of accounting and taxation standards. All these systems are often plagued by gaming and manipulations, making our modeling based on costly type falsification relevant. The project also has interdisciplinary impacts on computer science. Computer science theory is concerned with algorithmic manipulations. Given that such manipulations can be costly and that the abstract modeling of algorithms resembles that of mechanisms without transfers, the modeling and solution approach can be applied to the design of manipulation-proof algorithms.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.