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

本课题研究如何有效且公平地分配稀缺的社会资源。研究人员将研究在不同需求和资源可用性的背景下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的角度定义和量化理想的结果；如何预测不同家庭不同类型干预的结果；以及如何优化稀缺资源的配置，在公平概念、参与者偏好和整个服务提供系统创造的激励所限定的约束下，实现最佳的社会结果。该小组采用的算法方法将导致更有效和对社会有益地利用社区现有的有限资源，以减轻无家可归这一核心社会问题，同时尊重当前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司法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，并通过研究中代表性不足的人群的参与来扩大对计算机的参与。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，重点是定义考虑效率和公平的目标函数。他们将开发分析模型，分析代理在服务需求和脆弱性状态中是如何演变的，并对这些模型进行求解，以便在给定干预如何影响不同类型代理的不同假设的情况下，确定最优策略。利用这些模型，团队将研究“正义的代价”或为实现不同目标而产生的效率损失。理论模型将以大城市地区使用无家可归者服务的实际数据为依据。该团队还将研究有限社会资源最优动态分配中的新问题，仔细考虑如何在不同时间可能需要使用社会服务的代理集的优化资源分配过程中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）。该奖项反映了美国国家科学基金会的法定使命，并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，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.