用预测改进设施选址机制，兼顾公平与鲁棒性。
Mechanism Design for Facility Location using Predictions
- 引入预测信息优化设施选址，同时考虑最远距离和最低效用
- 新机制在预测准确时表现更优，预测错误时仍保持稳健
- 适用于需平衡公平性与抗干扰能力的多设施选址场景
我们研究了在最优设施位置预测基础上的设施选址机制设计。相比仅关注最大距离的传统视角，引入兼顾任意个体到设施最大距离与最低效用的平等视角，提供了全新洞察。沿用以往研究中的性能衡量标准：一致性（预测准确时最坏情况）与鲁棒性（预测是否准确下的最坏情况）。通过分析带预测机制可能表现糟糕的情形，我们设计了更具鲁棒性的新机制，并证明可通过调节参数在鲁棒性与一致性间进行权衡。进一步拓展至双设施问题，提出新型策略诱导机制，利用两个预测位置实现双设施定位，同时保证一致性和鲁棒性有界。
原文摘要 · Abstract (English)
We study mechanisms for the facility location problem augmented with predictions of the optimal facility location. We demonstrate that an egalitarian viewpoint which considers both the maximum distance of any agent from the facility and the minimum utility of any agent provides important new insights compared to a viewpoint that just considers the maximum distance. As in previous studies, we consider performance in terms of consistency (worst case when predictions are accurate) and robustness (worst case irrespective of the accuracy of predictions). By considering how mechanisms with predictions can perform poorly, we design new mechanisms that are more robust. Indeed, by adjusting parameters, we demonstrate how to trade robustness for consistency. We go beyond the single facility problem by designing novel strategy proof mechanisms for locating two facilities with bounded consistency and robustness that use two predictions for where to locate the two facilities.
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