资源分配中的认知公平与反事实公平
Epistemic vs. Counterfactual Fairness in Allocation of Resources
AI总结：
本文通过人类受试者实验比较资源分配中EF的反事实与认知松弛，发现认知EF被感知为更公平，并分析规模、结果平衡和认知努力对公平推理的影响。
摘要：
资源分配是众多社会决策场景的基础，从慈善捐赠的分配到在有意向的家庭之间配给有限的公共住房，均属此类。在此背景下，一个核心挑战是确保结果公平，而这往往需要平衡不同利益相关者相互冲突的偏好。尽管公平划分框架内已有大量关于理论和算法解决方案的研究，但其中许多工作忽视了个体对公平的主观感知。本研究聚焦于无嫉妒（EF）这一公平概念，它确保没有任何主体会根据自身偏好而偏好另一个主体的分配。虽然精确的EF分配并不总是可行，但人们已提出多种近似松弛，例如反事实EF和认知EF。通过一系列有人类参与者参与的实验，我们比较了人们对三种被广泛研究的EF反事实松弛与认知松弛的公平感知。研究结果表明，基于认知EF的分配比基于反事实松弛的分配被认为更公平。此外，我们还考察了多种因素，包括规模、结果的平衡性以及评估公平时涉及的认知努力，以及它们在不同处理组间推理复杂性中的作用。
英文摘要：
Resource allocation is fundamental to a variety of societal decision-making settings, ranging from the distribution of charitable donations to assigning limited public housing among interested families. A central challenge in this context is ensuring fair outcomes, which often requires balancing conflicting preferences of various stakeholders. While extensive research has been conducted on theoretical and algorithmic solutions within the fair division framework, much of this work neglects the subjective perception of fairness by individuals. This study focuses on the fairness notion of envy-freeness (EF), which ensures that no agent prefers the allocation of another agent according to their own preferences. While the existence of exact EF allocations may not always be feasible, various approximate relaxations, such as counterfactual and epistemic EF, have been proposed. Through a series of experiments with human participants, we compare perceptions of fairness between three widely studied counterfactual and epistemic relaxations of EF. Our findings indicate that allocations based on epistemic EF are perceived as fairer than those based on counterfactual relaxations. Additionally, we examine a variety of factors, including scale, balance of outcomes, and cognitive effort involved in evaluating fairness and their role in the complexity of reasoning across treatments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
