AI 中文总结
本研究提出公平性实施成本概念，即验证公平性所需采样量，并开发公平性引导的自适应分配算法，以在有限预算下高效选择最佳公平策略。
AI 中文摘要
服务组织使用仿真、试点研究和历史数据来选择在总体绩效与公平性之间取得平衡的服务策略。现有工作主要评估公平性的运营成本，即因限制可行策略集而导致的绩效损失。在本研究中，我们将实施成本识别并研究为公平性的一个独立且同等重要的维度，定义为验证公平性并可靠选择最佳公平策略所需的采样工作量。具体而言，我们考虑在基于平均绩效、服务不足风险、分位数和上尾结果的公平性要求下的固定预算策略选择，并表明不同的公平性度量可以诱导相同的公平策略集和相同的运营成本，同时需要显著不同数量的证据，因为它们的估计器具有不同的局部验证难度。我们进一步表明，在接近临界公平性容差时，实施所需的采样预算与到边界的距离的平方成反比缩放，从而产生有限预算的实施差距。这些结果表明，公平性度量、公平性容差和可用采样预算应联合设计，因为总体层面的公平性要求可能在运营上具有吸引力，但在可用预算下在统计上不可实施。为了将这一视角转化为实践，我们开发了一种公平性引导的自适应分配算法，该算法将样本导向控制错误选择的排序和公平性验证比较。在合成、呼叫中心和急诊科环境中的实验证明了本工作中识别的实施成本机制，并表明所提出的分配更有效地利用采样预算。
英文摘要
Service organizations use simulation, pilot studies, and historical data to select service policies that balance aggregate performance and fairness. Existing work primarily evaluates the operational cost of fairness, defined as the performance loss caused by restricting the feasible policy set. In this research, we identify and study implementation cost as a distinct and equally important dimension of fairness, defined as the sampling effort required to verify fairness and reliably select the best fair policy. Specifically, we consider fixed-budget policy selection under fairness requirements based on mean performance, under-service risk, quantiles, and upper tail outcomes, and show that different fairness metrics can induce the same fair policy set and the same operational cost while requiring substantially different amounts of evidence because their estimators have different local verification difficulty. We further show that, near a critical fairness tolerance, the required sampling budget for implementation scales inversely with the square of the distance to the boundary, creating a finite-budget implementation gap. These results imply that the fairness metric, the fairness tolerance, and the available sampling budget should be designed jointly, because a population-level fairness requirement may be operationally attractive but not statistically implementable at the available budget. To translate this perspective into practice, we develop a fairness-guided adaptive allocation algorithm that directs samples toward the ranking and fairness verification comparisons governing false selection. Experiments in synthetic, call center, and emergency department settings demonstrate the implementation cost mechanisms identified in this work and show that the proposed allocation uses the sampling budget much more effectively.
