NSF-BSF: Mechanism Design for All

NSF-BSF：所有人的机制设计

• 批准号: 2343922
• 负责人: Yannai Aharon Gonczarowski
• 金额: $ 46.24万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2343922&HistoricalAwards=false
• 关键词: NSF; BSF; Mechanism; Design

School choice programs that match students to schools are one example of what is called in economics a “matching market”. Many such matching markets use a centralized mechanism, or algorithm, to match applicants with institutions. Increasingly wider adoption of such mechanisms (not only in school choice programs but also in physician residency programs and others) results in a profile of participants in economic mechanisms that is more diverse than ever before. For this reason, the economic literature is slowly shifting from “idealized participants”, who are perfectly rational and never make mistakes, to studying these mechanisms both theoretically and empirically, taking into account that the participants are real and all too human. Yet, quite often, empirical and experimental studies are conducted on some of the best students in the country, either because they are run in labs at leading US universities, or because even studies analyzing real-world matching markets focus on those markets that match recent graduates with employers (for instance, resident doctors are assigned to hospitals). These students and graduates exhibit various background traits not necessarily found in individuals participating in real-life mechanisms: graduates are fairly well educated, have impressive reading comprehension skills in English, are trained in analytical reasoning, and have the ability to focus and follow complex instructions. Moreover, college-educated participants are much more likely to have trust in institutions; and they have a more uniform cultural background than the general population. It is unclear to what extent experiments that focus on introducing and explaining new mechanisms that are run on college-educated subjects (who are well trained and have the ability to follow new, complex instructions) would properly inform the design of mechanisms that are ultimately meant to be applied to everybody, including subjects with different backgrounds, abilities, and skills. This research studies (and aims to improve) the design of mechanisms for all people from all populations.Unfortunately, those agents who are less adept at strategizing in situations where the mechanism is not strategy-proof, are less likely to recognize a mechanism that is strategy-proof (and for which strategizing is not beneficial). These agents may belong to less advantaged groups, have less trust in institutions, or have fewer people in their social circles whom they can lean on to verify scientific claims. To reach an equitable outcome, it therefore does not suffice for the mechanism to be strategy-proof; its strategy-proofness must also be understood by all participants, from all populations. For example, the deferred acceptance mechanism, which is known to have desirable properties and yield desirable outcomes in theory, is anything but easy to understand for the people it is supposed to serve. A double-edged challenge here is therefore to (a) develop the theory of easy-to-understand mechanisms; and (b) empirically and repeatedly test their performance on a large enough set of subjects (e.g., broad online samples) to make it possible to study heterogeneity of outcomes. This project has three aims. First, to improve our empirical understanding of the way real people, from real populations, understand mechanisms (strategically and otherwise) and interact with them. Second, to improve our theoretical understanding of behavioral issues traditionally neglected in mechanism design. Finally, to theoretically derive, and empirically test, more efficient and inclusive mechanisms (and their descriptions) in such behavioral models.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.

与学校相匹配的学校选择课程是经济学中所谓的“匹配市场”的一个例子。许多这样的匹配市场使用集中机制或算法将应用程序与机构匹配。越来越广泛地采用了这种机制（不仅在学校选择计划中，而且在物理居住计划中，其他方面）都会导致参与者的经济机制比以往任何时候都更加潜水。因此，经济文献从“理想的参与者”逐渐转移，他们是完全理性的，从不犯错，从理论上和经验上研究这些机制，考虑到参与者是真实的，而且都是人性化的。然而，经常对该国一些最好的学生进行经验和实验研究，要么是因为它们是在领先的美国大学的实验室中运营的，要么是因为分析了现实世界中匹配的市场专注于将最近毕业生与员工与员工相匹配的市场（例如，居民医生被分配给医院）。这些学生和毕业生表现出各种背景特征，不一定在参与现实生活机制的个人中发现：毕业生受过良好的教育，具有令人印象深刻的英语阅读理解能力，接受了分析推理的培训，并且能够集中精力并遵循复杂的指导。此外，受过大学教育的参与者更有可能对机构有信任。它们的文化背景比一般人群更统一。目前尚不清楚在多大程度上集中于引入和解释在受过大学教育的学科（训练有素且具有遵循新的，复杂说明的能力的能力）上运行的新机制的实验，将适当地为所有人的机制设计，包括所有人，包括具有不同背景，能力和技能的受试者。这项研究（并旨在改善）所有人群的机制设计。这些代理人可能属于较少优势的群体，对机构的信任较少，或者在社交圈中拥有较少的人，他们可以依靠这些人来核实科学主张。为了达到公平的结果，这不足以使机制具有防策略；所有参与者也必须从所有人群中理解其防止策略。例如，延迟的接受机制在理论上具有理想的属性和产生理想的结果，这对于预期的人来说很容易理解。因此，这里的双重挑战是（a）发展易于理解机制的理论； （b）紧急并反复测试他们在足够大的受试者（例如广泛的在线样本）上的表现，以便研究结果的异质性。该项目有三个目标。首先，要改善我们对真实人群，实际人群的经验理解，了解机制（战略性和其他方式）并与他们互动。第二，为了提高我们对机制设计中传统上忽略的行为问题的理论理解。最后，在理论上得出并在经验测试中，在此类行为模型中提出了更有效和更具包容性的机制（及其描述）。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务。