Apprenticeship, Cooperation and Choice

学徒、合作与选择

• 批准号: 1951056
• 负责人: Drew Fudenberg
• 金额: $ 26.3万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951056&HistoricalAwards=false
• 关键词: Apprenticeship; Cooperation; Choice

This award funds several research projects in economic theory. Each project examines a specific question about the implications of self-interested behavior. The first project will help us understand how reciprocal altruism leads to cooperation in communities. The second project focuses on how to best structure apprenticeships and traineeships. The third project examines two different models of how individuals make decisions between choices. Taken as a whole, this research will help us understand how societies build cooperation; how to best train individuals for jobs in fields such as law, medicine, academia, and skilled trades; and will improve foundational models used in macroeconomics, finance, neuroscience, and psychology. The first project focuses on self-interested cooperation and introduces a new class of models of anonymous interaction. The project contributes both to the theory of repeated games and to the design of reputation systems. The project on apprenticeship employs contract theory and optimal control theory to understand key empirical features of apprenticeships as well as the implications of possible government regulations that would reduce apprentices’ hours and increase their pay. The project on choice under uncertainty will apply machine learning methods to experimental data to develop better models of choice under uncertainty. The second part of this final project will develop identification, estimation, and testing results for the drift diffusion model which is used to explain the observed correlation between probability of choice and time to decision.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.

这个奖资助了几个经济理论方面的研究项目。每个项目都考察了一个关于自利行为含义的具体问题。第一个项目将帮助我们理解互惠利他主义是如何导致社区合作的。第二个项目的重点是如何最好地构建学徒制和实习制。第三个项目考察了个人如何在选择之间做出决定的两种不同模型。总的来说，这项研究将帮助我们理解社会是如何建立合作的；如何最好地培训个人在法律、医学、学术和技术行业等领域的工作；并将改进宏观经济学、金融学、神经科学和心理学中使用的基础模型。第一个项目侧重于自利合作，并引入了一类新的匿名交互模型。该项目对重复博弈理论和声誉系统的设计都有贡献。学徒制项目采用契约理论和最优控制理论来理解学徒制的关键经验特征，以及可能减少学徒工时和增加学徒工资的政府法规的影响。不确定性下的选择项目将把机器学习方法应用到实验数据中，以开发更好的不确定性下的选择模型。这个最终项目的第二部分将开发漂移扩散模型的识别，估计和测试结果，该模型用于解释观察到的选择概率与决策时间之间的相关性。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

项目成果

Working to learn

工作学习

• DOI: 10.1016/j.jet.2021.105347
• 发表时间: 2021
• 期刊: Journal of Economic Theory
• 影响因子: 1.6
• 作者: Fudenberg, Drew;Georgiadis, George;Rayo, Luis
• 通讯作者: Rayo, Luis

Record-Keeping and Cooperation in Large Societies

• DOI: 10.1093/restud/rdab016
• 发表时间: 2021-03
• 期刊: The Review of Economic Studies
• 作者: Daniel Clark;D. Fudenberg;A. Wolitzky

Machine learning for evaluating and improving theories

用于评估和改进理论的机器学习

• DOI: 10.1145/3440959.3440962
• 发表时间: 2020
• 期刊: ACM SIGecom Exchanges
• 影响因子: 1
• 作者: Fudenberg, Drew;Liang, Annie
• 通讯作者: Liang, Annie

Testing the drift-diffusion model

• DOI: 10.1073/pnas.2011446117
• 发表时间: 2020-12-29
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Fudenberg, Drew;Newey, Whitney;Strzalecki, Tomasz
• 通讯作者: Strzalecki, Tomasz

Measuring the Completeness of Economic Models

衡量经济模型的完整性

• DOI: 10.1086/718371
• 发表时间: 2022
• 期刊: Journal of Political Economy
• 影响因子: 8.2
• 作者: Fudenberg, Drew;Kleinberg, Jon;Liang, Annie;Mullainathan, Sendhil
• 通讯作者: Mullainathan, Sendhil