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.

将学生与学校进行匹配的择校计划是经济学中所谓的“匹配市场”的一个例子，许多此类匹配市场使用集中机制或算法来将申请人与机构进行匹配（不仅如此）。择校项目以及住院医生项目等）导致经济机制的参与者比以往任何时候都更加多样化。因此，经济文献正在慢慢地从“理想化”转变，他们是完全理性和理性的。永远不要犯错误，要研究这些机制考虑到参与者是真实的并且太人性化，无论是在理论上还是在实证上，然而，实证和实验研究通常是针对该国一些最优秀的学生进行的，因为他们是在美国领先大学的实验室中进行的。 ，或者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匹配的市场（例如，住院医师被分配到医院），这些学生和毕业生表现出各种背景特征，这些特征不一定存在于参与现实世界的个人中。生活机制：毕业生受过良好教育，具有令人印象深刻的阅读理解能力英语，受过分析推理训练，有能力集中注意力并遵循复杂的指令，而且，受过大学教育的参与者比一般人更有可能信任机构；而且他们有更统一的文化背景。目前还不清楚，专注于引入和解释新机制的实验，在受过大学教育的受试者（受过良好训练并且有能力遵循新的、复杂的指令）上运行的实验将在多大程度上正确地为最终旨在实现的机制的设计提供信息适用于每个人，包括具有不同背景、能力和能力的主体这项研究研究（并旨在改进）针对所有人群的所有人的机制设计。不幸的是，那些不擅长在机制无法证明策略的情况下制定策略的代理人不太可能认识到一种机制。这些代理人可能属于弱势群体，对机构的信任度较低，或者他们的社交圈子中可以依靠的人较少，以验证科学主张。公平的结果，因此该机制不足以具有战略证明性；其战略证明性还必须被所有群体的所有参与者所理解，例如，延迟接受机制，众所周知，它在理论上具有理想的特性并产生理想的结果。因此，对于它所服务的人们来说，这是一个双刃剑的挑战，即（a）开发易于理解的机制理论；（b）根据经验反复测试其性能。足够大的主题集（例如，广泛的在线样本）使研究结果的异质性成为可能 该项目有三个目标：首先，提高我们对真实人群、理解机制（战略上和其他方面）和互动方式的实证理解。其次，提高我们对机制设计中传统上被忽视的行为问题的理论理解。最后，从理论上推导并实证检验此类行为模型中更有效和更具包容性的机制（及其描述）。该奖项反映了 NSF 的法定使命和目标。已被认为值得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来提供支持。