发表机构
Virginia Tech; Washington University in St. Louis（弗吉尼亚理工大学; 华盛顿大学圣路易斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探讨预测不确定性差异对稀缺社会资源分配的影响，构建相关数学模型，发现资源稀缺与充足时的优先分配策略反转，还评估两种分配策略的效率损失。
AI 中文摘要
新兴文献研究了一个关键问题：在分配稀缺社会资源时，预测何时以及如何发挥作用。我们研究该问题的一个新变体：当预测不确定性在不同群体间存在系统性差异时会发生什么？这种情况会在多种场景中出现，例如机器学习模型在不同人口统计群体间的准确率存在显著差异。我们表明，当这种不确定性与常用的二元社会资源分配效益衡量指标结合时，会对资源分配产生严重影响。我们构建了一种考虑异质性预测不确定性的稀缺资源分配数学模型，并分析其对分配机制和实现的群体层面效益的启示。我们发现，当资源非常稀缺时，最大边际效益（MMB）分配策略会优先选择预测不确定性较低的个体，即便他们的初始基础状态完全相同；但当资源充足时，优先顺序会反转，转向预测不确定性较高的个体。我们在PISA教育测试数据集上说明了研究结果的启示。我们的发现对受局部正义理论影响的多个领域的优先政策分配结果具有重要意义，包括公共教育资源分配、医疗分诊和无家可归者服务。它们还揭示了稀缺资源分配伦理中的一个新道德困境：仅基于对个人未来的预测不确定性而将资源分配给一人而非另一人是否公正？我们还评估了最大边际效益（MMB）和优先脆弱性（VF）分配策略下的效率损失，我们的模型预测在所有资源水平下都会出现效率损失，尤其在资源匮乏的环境中更为明显。
英文摘要
An emerging literature examines the critical question of when and how prediction can be useful in allocating scarce societal resources. We examine a novel variant of this question: What happens when predictive uncertainty differs systematically across the population? This can occur in several situations; for example, when machine learning models have significantly different accuracies across different demographics. We show that this uncertainty has serious implications for resource allocation when coupled with commonly used binary measures of societal benefit from allocation. We formulate a novel mathematical model of scarce resource allocation that accounts for heterogeneous predictive uncertainties and analyze implications for both the allocation mechanism and the realized population-level benefits. We find that when resources are very scarce, maximum marginal benefit (MMB) prioritization favors individuals with lower predictive uncertainty even at the identical underlying initial state. However, we observe a flip in prioritization when resources are abundant, targeting higher-uncertainty individuals. We illustrate the implications of our results on the PISA educational testing dataset. Our findings have meaningful ramifications for the distributional outcomes of prioritization policies in many domains touched by the theory of local justice, including the allocation of public education resources, medical triage, and homelessness services. They also reveal a new moral dilemma in the ethics of scarce resource allocation - is it just to allocate a resource to one person over another solely based on predictive uncertainty about their futures? We also assess efficiency losses under both MMB and the vulnerability-first (VF) prioritization. Our model predicts efficiency losses across all resource levels, but particularly in low-resource settings.
