Collaborative Research: Disentangling Exploration from Exploitation

合作研究：将探索与利用分开

• 批准号: 1949381
• 负责人: Leeat Yariv
• 金额: $ 19.95万
• 依托单位: Princeton University
• 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: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949381&HistoricalAwards=false
• 关键词: Collaborative; Research; Disentangling; Exploration; Exploitation

This group of collaborative awards funds a project in economic theory. The project will develop a new approach to analyze how people make decisions between trying new experiences or continuing with long standing choices. The new approach applies to many real world situations that are not well explained with existing models. For instance, an investor might invest in one stock while gathering information on the returns to a different investment. A policymaker might explore new alternatives while implementing existing policy. An employee might search for a new job while continuing her current employment. The model applies to these situations where an individual chooses between multiple projects with unknown rewards and can invest in one to earn a reward while also observing the rewards of different projects. The new model is an alternative to a large class of models from economics and statistics that has studied a different kind of experimentation problem, often referred to as the bandit problem. In a bandit problem, the decision maker can only learn through investing. She gains information only about the project that she chooses: what she learns is linked to what she earns. The result is a well-known trade-off between exploration and exploitation: the decision - maker may desire investment in a low reward project because it offers valuable learning. The existing group of bandit models have been widely applied. The new research funded by this grant could result in deeper understanding of a similarly wide range of applications, including applications that could enhance national security and strengthen the US economy. The research team will consider three environments that offer a particularly stark contrast with the classical experimentation framework. First, when experimentation is conducted by individuals, the optimal alternative is always eventually discovered. This contrasts with the classical result of incomplete learning in classical experimentation with discounting. However, behavior is more complex than in standard experimentation environments since a simple index (such as the Gittins index,which characterizes solutions in the classical framework) need not exist. Second, teams composed of similar members are no longer subject to free-rider problems: when observation and exploitation are separated, teams achieve efficient experimentation. The team will also analyze questions that cannot be addressed in the standard setting. Specifically, the project explores a delegation environment in which there are two agents: the executive (Doer), who chooses the project to be implemented, and the intelligence agency (Observer), who chooses what project to look at. Because the objectives of these two agents may be misaligned, delegated choices may yield either minimal or maximal experimentation depending on the nature of contact between the doer and the observer. Ultimately, the project has the potential to uncover novel features of experimentation that can be applied to various applications and tested against data.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.

这组合作奖资助了一个经济理论项目。该项目将开发一种新的方法来分析人们如何在尝试新体验或继续长期选择之间做出决定。新的方法适用于许多真实的世界的情况下，没有很好地解释与现有的模型。例如，投资者可能投资于一只股票，同时收集有关不同投资回报的信息。政策制定者可能会在执行现有政策的同时探索新的替代方案。员工可能会在继续现有工作的同时寻找新工作。该模型适用于这样的情况，即一个人在多个回报未知的项目之间进行选择，并可以投资于一个项目以获得回报，同时还可以观察不同项目的回报。新模型是经济学和统计学中一大类模型的替代品，这些模型研究了一种不同类型的实验问题，通常被称为强盗问题。在强盗问题中，决策者只能通过投资来学习。她获得的信息只与她选择的项目有关：她学到的东西与她的收入有关。其结果是众所周知的探索和开发之间的权衡：决策者可能希望投资于低回报项目，因为它提供了有价值的学习。现有的一组强盗模型已经得到了广泛的应用。由这笔赠款资助的新研究可以加深对类似广泛应用的理解，包括可以增强国家安全和加强美国经济的应用。研究小组将考虑三种与经典实验框架形成鲜明对比的环境。首先，当实验由个人进行时，最佳选择总是最终被发现的。这与经典实验中不完全学习的经典结果形成鲜明对比。然而，行为比在标准实验环境中更复杂，因为不需要存在简单的索引（例如Gittins索引，其表征经典框架中的解决方案）。其次，由相似成员组成的团队不再受搭便车问题的影响：当观察和开发分开时，团队可以实现有效的实验。该团队还将分析标准设置中无法解决的问题。具体来说，该项目探讨了一个委托环境，其中有两个代理人：执行者（Doer），谁选择要实施的项目，和情报机构（Observer），谁选择要看的项目。因为这两个代理人的目标可能是不一致的，委托的选择可能会产生最小或最大的实验取决于行为者和观察者之间的接触的性质。最终，该项目有可能发现新的实验功能，可应用于各种应用和数据测试。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Constrained Retrospective Search

受限回顾性搜索

• DOI: 10.1257/pandp.20211054
• 发表时间: 2021
• 期刊: AEA Papers and Proceedings
• 作者: Urgun, Can;Yariv, Leeat
• 通讯作者: Yariv, Leeat