We consider a setting in which a group of agents share resources that must be allocated among them in each discrete time period. Agents have time-varying demands and derive constant marginal utility from each unit of resource received up to their demand, with zero utility for any additional resources. In this setting, it is known that independently maximizing the minimum utility in each round satisfies sharing incentives (agents weakly prefer participating in the mechanism to not participating), strategyproofness (agents have no incentive to misreport their demands), and Pareto efficiency (Freeman et al. 2018). However, recent work (Vuppalapati et al. 2023) has shown that this max-min mechanism can lead to large disparities in the total resources received by agents, even when they have the same average demand. In this paper, we introduce credit fairness, a strengthening of sharing incentives that ensures agents who lend resources in early rounds are able to recoup them in later rounds. Credit fairness can be achieved in conjunction with either Pareto efficiency or strategyproofness, but not both. We propose a mechanism that is credit fair and Pareto efficient, and we evaluate its performance in a computational resource-sharing setting.

翻译：本文研究一种场景：一组智能体共享资源，这些资源必须在每个离散时间周期内分配给它们。智能体具有随时间变化的需求，并且从接收到的每单位资源中获得恒定的边际效用，直至满足其需求，超出需求的额外资源效用为零。在此场景下，已知独立最大化每轮最小效用的方法满足共享激励（智能体参与机制的效用不低于不参与）、策略证明性（智能体没有虚报需求的动机）和帕累托效率（Freeman等人，2018）。然而，近期研究（Vuppalapati等人，2023）表明，这种最大最小机制可能导致智能体获得的总资源量存在巨大差异，即使它们具有相同的平均需求。本文提出信用公平性——一种对共享激励的强化准则，确保在早期轮次中借出资源的智能体能够在后续轮次中收回资源。信用公平性可以与帕累托效率或策略证明性中的任一性质同时实现，但无法同时满足两者。我们提出一种兼具信用公平性与帕累托效率的机制，并在计算资源共享场景中评估其性能。