NSF-BSF: Mechanism Design for All

为学生和学校牵线搭桥的择校项目就是经济学中所说的“匹配市场”的一个例子。许多这样的配对市场使用一个集中的机制或算法来为申请者和机构牵线搭桥。越来越广泛地采用这种机制(不仅在择校方案中，而且在医生实习方案和其他方案中)，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完全理性、从不出错的“理想化参与者”，转向从理论和经验两方面研究这些机制，同时考虑到参与者是真实的，而且太人性化了。然而，很多时候，实证和实验研究是在美国一些最优秀的学生身上进行的，要么是因为他们是在美国顶尖大学的实验室里进行的，要么是因为即使是分析现实世界配对市场的研究也聚焦于那些将应届毕业生与雇主配对的市场(例如，住院医生被分配到医院)。这些学生和毕业生表现出了参与现实生活机制的个人所不具备的各种背景特征：毕业生受过相当好的教育，拥有令人印象深刻的英语阅读理解技能，接受过分析推理培训，并有能力专注于并遵循复杂的指令。此外，受过大学教育的参与者更有可能信任机构；他们的文化背景比一般人更一致。目前尚不清楚，专注于引入和解释在受过大学教育的受试者(受过良好培训并有能力遵循新的、复杂的指令)上运行的新机制的实验在多大程度上会正确地为最终旨在应用于每个人的机制的设计提供信息，包括具有不同背景、能力和技能的受试者。这项研究研究(并旨在改进)所有人群中所有人的机制设计。不幸的是，那些在机制不是防策略的情况下不太擅长制定战略的代理人，不太可能认识到一个不防策略的机制(而且制定战略对他们没有好处)。这些代理人可能属于不那么有利的群体，对机构不太信任，或者在他们的社交圈里可以依靠的人更少，以验证科学主张。因此，为了取得公平的结果，该机制不受战略影响是不够的；来自所有人口的所有参与者也必须理解其不受战略影响。例如，众所周知，延期接受机制在理论上具有令人满意的性质和产生令人满意的结果，但对于它应该服务的人来说，却一点也不容易理解。因此，这里的一个双刃剑挑战是：(A)发展易于理解的机制理论；(B)在足够大的一组受试者(例如，广泛的在线样本)上反复经验性地测试它们的表现，以使研究结果的异质性成为可能。这个项目有三个目标。首先，改善我们对真实人群中真实人群理解机制(从战略上和其他方面)以及与他们互动的方式的经验理解。第二，提高我们对机制设计中传统上被忽视的行为问题的理论认识。最后，从理论上推导和实证检验这种行为模式中更有效和更包容的机制(及其描述)。这一奖项反映了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。

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.