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

这个项目研究有效和公平地分配稀缺的社会资源的算法。研究人员将在不同的需求和资源可获得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：如何从效率和公平两方面界定和量化理想的结果；如何预测不同家庭的不同类型干预措施的结果；以及如何在公平概念、参与者的偏好和整个服务提供系统创造的激励措施所界定的约束下，优化稀缺资源的分配，以实现最佳的社会成果。该小组采取的算法办法将使社区能够更有效和对社会有益地利用有限的资源，以缓解无家可归这一核心社会问题，同时尊重现有分配机制所依据的需要和公平的概念。该项目是首批探索如何将地方正义原则应用于追求社会伦理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、人工智能和机制设计促进社会公益的对话。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。该团队将开发新的模型，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考虑的目标函数。他们将开发代理如何在其服务需求和脆弱性状态中演变的分析模型，并求解这些模型，以便在给定关于干预如何影响不同类型的代理的不同假设的情况下，表征最优策略。使用这些模型，团队将研究为实现不同目标而产生的“正义代价”或效率损失。理论模型将以一个主要大都市地区无家可归者服务使用情况的真实数据为依据。该团队还将研究有限社会资源的最优动态分配中的新问题，仔细考虑如何在可能需要在不同时间使用社会服务的代理集合上优化资源分配过程中如何使用对代理结果的整个概率分布的预测(而不仅仅是点估计)。这一奖项反映了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 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.