Studies on the Data-Driven Economy and School Choice

皮姓氏：CheTitle：数据驱动的经济与学校选择研究建议ID：1851821摘要本研究由几个项目组成。第一个项目着眼于数据驱动的建议如何为我们的决策提供信息，以及决策如何反过来影响为建议收集的数据。这个项目有助于理解和评估快速增长的信息技术的影响，并利用它来指导我们的日常决策。开发的框架还促进了人们对互联网平台如何从个人那里收集数据并使用这些数据来推荐产品或做出招聘决定的了解。第二个项目着眼于学生的学校申请，认为学生在这些申请中犯了错误。这些错误反过来影响了申请程序中的预测改革。第三和第四个项目是理论工具，将对从事相关领域工作的研究人员有用。第一个项目开发了一个概念框架，用于理解数据驱动的建议，这些建议通过考虑建议和我们的决定之间的反馈机制，越来越多地指导我们的日常决策。理解这一反馈过程确定了外部性和潜在低效的性质，从而加深了对数据驱动建议指导下的新市场的理解和可能的政策影响。这项研究还解决了人们的担忧，即雇主越来越依赖第三方算法来筛选求职者，这可能会对就业包容产生不利影响。第二个项目通过考虑学生在申请过程中犯的错误，提出了学校选择的机制设计，并将该框架用于基于排名和抽签的学校匹配。第三和第四个项目推进了动态说服和单调比较静态分析主题的最先进研究，这些主题是经济理论的核心兴趣。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。

PI Last Name: CheTitle: Studies on the Data-Driven Economy and School ChoiceProposal ID: 1851821AbstractThe research consists of several projects. The first project looks at how data-driven recommendations inform our decisions and how decisions in turn affect what data is collected for the recommendations. This project is useful in understanding and evaluating the implications of the rapidly growing information technology and its use to guide our every day decisions. The developed framework also advances the knowledge on how internet platforms may collect data from individuals and use them to recommend products or to make hiring decisions. The second project looks at school applications by students and considers that students make mistakes in these applications. These mistakes in turn affect forecasting reforms in the application procedures. The third and fourth projects are theoretical tools that will be useful for researchers that work in related areas. The first project develops a conceptual framework for understanding data-driven recommendations that increasingly guide our every-day decisions by considering the feedback mechanism between the recommendations and our decisions. Understanding this feedback process identifies the nature of externalities and potential inefficiencies, leading to deeper understanding and possible policy implications about the new markets guided by data-driven recommendations. The research also addresses the concern that the increasing reliance by employers on third-party algorithms to screen job candidates may adversely affect inclusion in employment. The second project advances mechanism design in school choice by considering that students make mistakes in applications and uses this framework for school matching based on rankings and lottery. The third and fourth projects advance the state-of-the-art research in dynamic persuasion and monotone comparative statics analysis topics that are of central interest in economic theory.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.