Learning Decision Rules in Shifting Environments

学习不断变化的环境中的决策规则

• 批准号: 2242876
• 负责人: Stefan Wager
• 金额: $ 44.91万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242876&HistoricalAwards=false
• 关键词: Learning; Decision; Rules; Shifting; Environments

This research project will develop new methods and software for data-driven decision making under environmental shift. Most work in data-driven decision making fundamentally relies on the stability of the statistical environment. Available tools for data-driven decision making generally assume that future environments in which decision rules will be deployed resemble the past environment where data was collected. Many real-world applications, however, display significant environmental shift due to various factors. The newly developed methods will expand researchers' ability to apply data-driven decision making to systems with environmental shift. Possible application areas range from medical settings and social programs to online marketplaces. Educational activities will include the training of graduate students and the development of learning resources based in part on the results of this research. All research products will be disseminated via publicly available repositories, and software will be released under an open-source license. This research project will provide a practical deep learning-based framework for learning decision rules that is robust to unknown distributional shifts. The project will develop methods for learning decision rules in settings with unknown distributional shifts, and where some sub-populations may be under- or over-sampled according to unobservable characteristics. Consider, for example, a volunteer-based study on the effects of antidepressants among patients suffering from depression, where motivation to take steps to fight depression could be an unobserved attribute that is overrepresented in the study population – and so the set of people we are able to collect data on may differ from the full patient population along some important but unobservable attributes. The project also will investigate learning decision rules under distributional shifts that naturally arise via equilibrium behavior in social settings with agents who interact with each other (e.g., by buying and selling goods to each other in a marketplace) and provide methods to address the resulting challenges. Overall, this project will provide new methodological and software solutions – as well as associated educational resources – that will expand the class of problems where data-driven decision making can be successfully deployed.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.

该研究项目将开发新的方法和软件，用于在环境变化下以数据驱动的决策制定。数据驱动决策中的大多数工作从根本上依赖于统计环境的稳定性。用于数据驱动决策的可用工具通常假定将未来部署规则部署的环境类似于收集数据的过去环境。但是，由于各种因素，许多现实世界应用都显示出明显的环境变化。新开发的方法将扩大研究人员将数据驱动决策应用于环境转变的系统的能力。可能的应用领域范围从医疗环境和社会计划到在线市场。教育活动将包括对研究生的培训以及基于本研究结果的学习资源的发展。所有研究产品将通过公开可用的存储库进行传播，并且软件将根据开源许可发布。该研究项目将为学习决策规则提供一个实用的基于深度学习的框架，该框架对未知的分配变化是可靠的。该项目将在具有未知分配变化的设置中开发学习决策规则的方法，并且根据无法观察到的特征，某些子选集可能会被不足或过度采样。例如，考虑一下，基于志愿者对抑郁症患者抗抑郁药的影响的研究，在研究人群中，采取措施对抗抑郁症的动机可能是一种未观察到的属性，在研究人群中代表过多，因此，我们能够收集的一组人可能会沿着一些重要但不可吸引的属性属性属于所有患者人群来差异。该项目还将调查在分配转变下的学习决策规则，这些决定自然会通过社交环境中的同等行为与彼此互动的代理商（例如，通过在市场中购买和销售商品相互互动），并提供解决挑战的方法。总体而言，该项目将提供新的方法论和软件解决方案以及相关的教育资源，这些解决方案将扩大可以成功部署数据驱动决策的问题类别。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。