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.

将学生与学校相匹配的择校计划是经济学中所谓的“匹配市场”的一个例子。许多这样的匹配市场使用集中的机制或算法来匹配申请人和机构。越来越广泛地采用这种机制（不仅在学校选择方案中，而且在医生住院方案和其他方案中），导致经济机制的参与者比以往任何时候都更加多样化。出于这个原因，经济学文献正在慢慢地从“理想化的参与者”（他们完全理性，从不犯错）转向从理论和经验上研究这些机制，考虑到参与者是真实的，而且太人性化了。然而，很多时候，实证和实验研究都是在美国最优秀的学生身上进行的，要么是因为这些研究是在美国一流大学的实验室里进行的，要么是因为即使是分析现实世界匹配市场的研究也关注那些将应届毕业生与雇主相匹配的市场（例如，住院医生被分配到医院）。这些学生和毕业生表现出各种背景特征，这些特征在参与现实生活机制的个人中不一定能找到：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，受过分析推理训练，并有能力集中注意力并遵循复杂的指令。此外，受过大学教育的参与者更有可能信任机构;他们比普通人群有更统一的文化背景。目前还不清楚，那些专注于引入和解释在受过大学教育的受试者（他们受过良好训练，有能力遵循新的复杂指令）身上运行的新机制的实验，在多大程度上会为最终适用于所有人（包括具有不同背景、能力和技能的受试者）的机制设计提供适当的信息。这项研究研究（并旨在改善）所有人群中所有人的机制设计。不幸的是，那些不太擅长在机制不防策略的情况下制定策略的代理人，不太可能识别出防策略的机制（并且制定策略是无益的）。这些代理人可能属于不太受欢迎的群体，对机构不太信任，或者在他们的社交圈中，他们可以依靠来验证科学主张的人很少。因此，为了取得公平的结果，该机制不受战略影响是不够的;它的不受战略影响还必须得到来自所有人口的所有参与者的理解。例如，延迟接受机制被认为具有理想的属性，并在理论上产生理想的结果，但对于它应该服务的人来说，这一机制却很难理解。因此，这里的一个双刃剑挑战是（a）发展易于理解的机制的理论;和（B）在足够大的受试者集合上经验地和重复地测试它们的性能（例如，广泛的在线样本），以研究结果的异质性。该项目有三个目标。第一，提高我们对来自真实的人群的真实的人如何理解机制（战略性的和其他方面的）并与之互动的经验性理解。第二，提高我们对传统上在机制设计中被忽视的行为问题的理论认识。最后，从理论上推导出，并通过实证检验，在这种行为模型中，更有效和包容的机制（及其描述）。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。