Envy-freeness has become the cornerstone of fair division research. In settings where each individual is allocated a disjoint share of collective resources, it is a compelling fairness axiom which demands that no individual strictly prefer the allocation of another individual to their own. Unfortunately, in many real-life collective decision-making problems, the goal is to choose a (common) public outcome that is equally applicable to all individuals, and the notion of envy becomes vacuous. Consequently, this literature has avoided studying fairness criteria that focus on individuals feeling a sense of jealousy or resentment towards other individuals (rather than towards the system), missing out on a key aspect of fairness. In this work, we propose a novel fairness criterion, individual harm ratio, which is inspired by envy-freeness but applies to a broad range of collective decision-making settings. Theoretically, we identify minimal conditions under which this criterion and its groupwise extensions can be guaranteed, and study the computational complexity of related problems. Empirically, we conduct experiments with real data to show that our fairness criterion is powerful enough to differentiate between prominent decision-making algorithms for a range of tasks from voting and fair division to participatory budgeting and peer review.

翻译：嫉妒自由性已成为公平分配研究的基石。在每位个体被分配集体资源中不相交份额的场景中，它是一个引人注目的公平性公理，要求没有个体严格偏好其他个体的分配胜过自己的分配。然而，在许多现实生活的集体决策问题中，目标是选择一个（共同的）公共结果，该结果平等地适用于所有个体，此时嫉妒的概念变得空洞无物。因此，现有文献一直避免研究那些关注个体对其他个体（而非对系统）感到嫉妒或怨恨的公平性标准，从而忽略了公平性的一个关键方面。在本工作中，我们提出了一种新颖的公平性准则——个体伤害比率，其灵感来源于嫉妒自由性，但适用于广泛的集体决策场景。在理论上，我们确定了能够保证该准则及其群体扩展形式的最小条件，并研究了相关问题的计算复杂性。在实证上，我们利用真实数据进行了实验，结果表明我们的公平性准则足够强大，能够区分从投票和公平分配到参与式预算和同行评审等一系列任务中多种主流决策算法。