在资源不足的情况下为有能力的设施选址的机制设计

Gennaro Auricchio, Harry J. Clough, Jie Zhang
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摘要

本文探讨了总设施容量小于代理数量的 $m$ 有能力设施位置问题的机制设计问题。按照 Aziz 等人概述的框架,设施位置的社会福利通过先到先得(FCFS)博弈来确定,在博弈中,一旦设施位置确定,代理就会展开竞争。当设施数量为 $m > 1$ 时,FCFS 博弈的纳什均衡(NE)并不是唯一的,这使得代理的效用和真实性的概念变得不明确。为了解决这些问题,我们考虑了绝对真实的机制,即无论在 FCFS 博弈过程中使用何种策略,都能防止代理人误报的机制。我们将这一更严格的真实性要求与均衡稳定(ES)机制的概念相结合,后者是指社会福利不依赖于 FCFS 博弈的 NE 的机制。我们证明了百分数机制是绝对真实的,并确定了它们成为 ES 机制的条件。我们还证明了每种 ES 百分比机制的近似率都是有界的,并确定了其值。值得注意的是,当所有设施的容量相同且代理数量足够大时,有可能实现小于 1+frac{1}{2m-1}$ 的近似率。最后,我们将研究扩展到了高维问题。在这个框架下,我们证明了 ES 百分位机制的类别甚至受到了更大的限制,并描述了既是 ES 又是绝对真实的机制的特征。我们还通过实证评估了代理人作为分布样本时的机制性能,进一步支持了我们的发现。
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Mechanism Design for Locating Facilities with Capacities with Insufficient Resources
This paper explores the Mechanism Design aspects of the $m$-Capacitated Facility Location Problem where the total facility capacity is less than the number of agents. Following the framework outlined by Aziz et al., the Social Welfare of the facility location is determined through a First-Come-First-Served (FCFS) game, in which agents compete once the facility positions are established. When the number of facilities is $m > 1$, the Nash Equilibrium (NE) of the FCFS game is not unique, making the utility of the agents and the concept of truthfulness unclear. To tackle these issues, we consider absolutely truthful mechanisms, i.e. mechanisms that prevent agents from misreporting regardless of the strategies used during the FCFS game. We combine this stricter truthfulness requirement with the notion of Equilibrium Stable (ES) mechanisms, which are mechanisms whose Social Welfare does not depend on the NE of the FCFS game. We demonstrate that the class of percentile mechanisms is absolutely truthful and identify the conditions under which they are ES. We also show that the approximation ratio of each ES percentile mechanism is bounded and determine its value. Notably, when all the facilities have the same capacity and the number of agents is sufficiently large, it is possible to achieve an approximation ratio smaller than $1+\frac{1}{2m-1}$. Finally, we extend our study to encompass higher-dimensional problems. Within this framework, we demonstrate that the class of ES percentile mechanisms is even more restricted and characterize the mechanisms that are both ES and absolutely truthful. We further support our findings by empirically evaluating the performance of the mechanisms when the agents are the samples of a distribution.
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