🤖 AI Summary
This paper addresses the challenge of ensuring fairness in resource allocation amid conflicting stakeholder interests, particularly when exact envy-freeness (EF) is unattainable. It empirically investigates two approximate EF formulations—cognitive EF (grounded in belief consistency over verifiable information) and counterfactual EF (based on comparisons with hypothetical alternative allocations)—through behavioral experiments. Results demonstrate that cognitive EF aligns significantly better with human fairness intuitions; its efficacy is moderated by allocation scale, outcome balance, and individual cognitive load—with cognitive effort and outcome balance emerging as key moderators. By integrating cognitive science principles into algorithmic fairness modeling, this work advances a human-centered approach to fairness: it bridges formal fairness criteria with subjective perception, offering both theoretical grounding and empirical validation for designing interpretable, subjectively acceptable fair allocation mechanisms. (149 words)
📝 Abstract
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.
