Dynamic resource allocation in multi-agent settings often requires balancing efficiency with fairness over time--a challenge inadequately addressed by conventional, myopic fairness measures. Motivated by behavioral insights that human judgments of fairness evolve with temporal distance, we introduce a novel framework for temporal fairness that incorporates past-discounting mechanisms. By applying a tunable discount factor to historical utilities, our approach interpolates between instantaneous and perfect-recall fairness, thereby capturing both immediate outcomes and long-term equity considerations. Beyond aligning more closely with human perceptions of fairness, this past-discounting method ensures that the augmented state space remains bounded, significantly improving computational tractability in sequential decision-making settings. We detail the formulation of discounted-recall fairness in both additive and averaged utility contexts, illustrate its benefits through practical examples, and discuss its implications for designing balanced, scalable resource allocation strategies.

翻译：在多智能体环境中进行动态资源分配时，常常需要在效率与长期公平性之间取得平衡——这一挑战是传统的短视公平性度量方法所未能充分解决的。受行为科学中关于人类公平判断随时间距离演化的见解启发，我们提出了一种融合过去折扣机制的新型时序公平性框架。通过对历史效用施加可调节的折扣因子，我们的方法能够在瞬时公平性与完美记忆公平性之间进行连续插值，从而同时捕捉即时结果与长期公平考量。除了更贴近人类对公平的感知外，这种过去折扣方法还能确保增广状态空间保持有界，显著提升了序贯决策场景中的计算可处理性。我们详细阐述了在加性和平均效用两种情境下的折扣记忆公平性形式化表述，通过实际案例说明其优势，并讨论了该方法对设计均衡、可扩展资源分配策略的启示。