衡量和减轻资源分配中的群体不平等

Arya Farahi, Angela Ting, Yingchen Ma
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摘要

资源分配是社会经济治理的一个组成部分,深刻影响个人繁荣,并有可能减轻或加剧社会经济差距。本文通过回答两个基本问题来解决有限资源在个体之间公平分配的挑战:(1)如何准确测量和测试群体差异;(2)如何在确保群体公平的同时优化分配资源。我们提出了群体受益人差距(GBD)指标,这是一种评估工具,旨在系统地衡量二元受益人/非受益人背景下的不平等。GBD为决策者和规划者提供了一个强大的工具,从群体平等的角度来审计社会项目和优化政策。我们认为,即使在社会福利约束下,功利主义决策者也不能完全消除群体差异。为了解决这个问题,我们提出了一个新的资源分配优化模型,称为a - farm(渐近公平分配资源模型),具有渐近群公平保证。a - farm将个体划分为不同的、不重叠的单元,并基于基于效用的分配机制在这些单元之间分配资源。最后,我们使用模拟和现实世界的数据来评估我们提出的算法的性能。我们的研究结果表明,a - farm使决策者能够(1)在群体公平约束下实现效率最大化,(2)进行公平-效率的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring and Mitigating Group Inequalities In Resource Allocation
Resource allocation, an integral part of socio-economic governance, profoundly influences individual prosperity and has the potential to mitigate or exacerbate socioeconomic disparities. This paper addresses the challenge of equitably allocating finite resources among individuals by answering two fundamental questions: (1) how to accurately measure and test group disparities and (2) how to optimally distribute resources while ensuring group fairness. We propose the Group Beneficiary Disparity (GBD) metric – an evaluation tool engineered to systematically gauge inequalities in a binary beneficiary/non-beneficiary context. The GBD provides decision-makers and planners with a powerful tool to audit social programs and optimize policies from a lens of group equality. We argue that utilitarian decision-makers cannot fully eliminate group disparities even when operating under social welfare constraints. To address this issue, we propose a new resource allocation optimization model, called A-FARM (Asymptotically Fair Allocation of Resources Model), with asymptotic group fairness guarantees. A-FARM partitions individuals into distinct, non-overlapping units and distributes resources among these units based on a utility-based allocation mechanism. Finally, we evaluate the performance of our proposed algorithm using both simulated and real-world data. Our results demonstrate that, A-FARM enables decision-makers to (1) achieve maximume efficiency under group fairness constrain and (2) perform a fairness-efficiency trade-off.
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