RI: Small: Efficient and Just Allocation of Scarce Societal Resources, and Applications to Homelessness

该项目研究有效和公正分配稀缺社会资源的算法。研究人员将在不同需求和资源可用性的背景下研究与算法决策相关的基本问题。关键问题包括如何界定和量化效率和公平方面的理想成果;如何预测不同家庭不同类型干预措施的成果;以及如何优化稀缺资源的分配，在公平概念、参与者偏好和整个服务提供系统创造的激励措施所界定的制约因素下实现最佳社会成果。该团队采取的算法方法将导致更有效和更有益于社会地使用社区可用的有限资源，以减轻无家可归的核心社会问题，同时尊重当前分配机制所基于的需求和公平概念。该项目是第一个探索如何将地方正义原则应用于追求社会道德算法干预的项目之一，有助于正在进行的关于公平机器学习和人工智能以及社会公益机制设计的对话。该项目还有助于培养研究生，为本科生提供研究经验，并通过让代表性不足的人群参与研究来扩大对计算的参与。该团队将开发新模型，以了解不同算法技术对稀缺社会资源分配的影响，重点是定义兼顾效率和公平的目标函数。他们将开发代理如何在他们的服务需求，他们的脆弱性状态演变的分析模型，并解决这些模型，以表征最佳的政策，给定不同的假设干预如何影响不同类型的代理。利用这些模型，该小组将研究“正义的代价”或为实现不同目标而造成的效率损失。理论模型将由一个大都市地区无家可归者服务使用情况的真实的数据提供信息。该小组还将研究有限社会资源的最佳动态分配的新问题，仔细考虑如何预测整个概率分布的结果，为代理人（而不仅仅是点估计）可用于优化可能需要在不同时间使用社会服务的多组代理的资源分配过程。该奖项反映了NSF的法定使命，并被认为值得支持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。

This project studies algorithms for efficient and just allocation of scarce societal resources. The investigators will study fundamental questions related to algorithmic decision making in the context of disparate needs and resource availability. Key questions include how to define and quantify desirable outcomes in terms of both efficiency and fairness; how to predict outcomes of different types of interventions for different households; and how to optimize the allocation of scarce resources that achieve the best societal outcomes under constraints defined by notions of fairness, preferences of participants, and incentives created by service delivery systems as a whole. The algorithmic approach taken by the team will lead to more efficient and socially beneficial use of the limited resources available to communities for mitigating the central social problem of homelessness, while respecting the notions of need and fairness on which current allocation mechanisms are based. This project is among the first to explore how principles of local justice can be applied in the pursuit of ethical algorithmic intervention in society, contributing to the ongoing dialogue on fair machine learning and AI and Mechanism Design for Social Good. The project also contributes to the training of graduate students, research experiences for undergraduates, and broadening participation in computing through involvement of underrepresented populations in the research.The team will develop new models to understand the effects of different algorithmic techniques for allocation of scarce societal resources, with a focus on defining objective functions that take into account both efficiency and considerations of fairness. They will develop analytical models of how agents evolve in their needs for services, and their vulnerability states, and solve these models in order to characterize optimal policies, given different assumptions about how intervention affects different types of agents. Using these models, the team will study the "price of justice" or the efficiency loss incurred in order to achieve different objectives. The theoretical modeling will be informed by real data on the use of homelessness services in a major metropolitan area. The team will also study new problems in optimal dynamic allocation of limited societal resources, considering carefully how predictions about the entire probability distributions of outcomes for agents (rather than just point estimates) can be used in the process of optimizing resource allocation over sets of agents that could need to use social services at different times.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.