Learning Decision Rules in Shifting Environments

该研究项目将为环境变化下的数据驱动决策开发新的方法和软件。数据驱动决策方面的大部分工作从根本上依赖于统计环境的稳定性。可用于数据驱动的决策制定的工具通常假设将在其中部署决策规则的未来环境类似于过去收集数据的环境。然而，由于各种因素，许多现实世界的应用程序显示出显著的环境变化。新开发的方法将扩大研究人员将数据驱动的决策应用于环境变化系统的能力。可能的应用领域从医疗设置和社交项目到在线市场。教育活动将部分根据这项研究的结果，包括对研究生的培训和学习资源的开发。所有研究产品将通过公开可用的存储库发布，软件将在开放源码许可证下发布。这一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，该框架对未知的分布变化具有健壮性。该项目将开发在分布漂移未知的情况下学习决策规则的方法，以及根据不可观察的特征可能对某些子总体进行欠采样或过采样的情况。例如，考虑一项关于抑郁症患者服用抗抑郁药物的效果的志愿者研究，其中采取措施对抗抑郁的动机可能是一个未被观察到的属性，在研究人群中被过度代表-因此我们能够收集数据的人群可能在一些重要但不可观察到的属性上与整个患者人群有所不同。该项目还将调查分配变化下的学习决策规则，这些变化通过社会环境中与彼此交互(例如，通过在市场上相互购买和销售商品)的均衡行为自然产生，并提供解决由此产生的挑战的方法。总体而言，该项目将提供新的方法和软件解决方案-以及相关的教育资源-将扩大数据驱动的决策可以成功实施的问题类别。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。

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.