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Asymptotically Optimal Strategy-Proof Mechanisms for Two-Facility Games

机译:两种设施游戏的渐近最优战略机制

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We consider the problem of locating facilities in a metric space to serve a set of selfish agents. The cost of an agent is the distance between her own location and the nearest facility. The social cost is the total cost of the agents. We are interested in designing strategy-proof mechanisms without payment that have a small approximation ratio for social cost. A mechanism is a (possibly randomized) algorithm which maps the locations reported by the agents to the locations of the facilities. A mechanism is strategy-proof if no agent can benefit from misreporting her location in any configuration. This setting was first studied by Procaccia and Tennen-holtz [21]. They focused on the facility game where agents and facilities are located on the real line. Alon et al. studied the mechanisms for the facility games in a general metric space [1]. However, they focused on the games with only one facility. In this paper, we study the two-facility game in a general metric space, which extends both previous models. We first prove an fi(n) lower bound of the social cost approximation ratio for deterministic strategy-proof mechanisms. Our lower bound even holds for the line metric space. This significantly improves the previous constant lower bounds [21,17]. Notice that there is a matching linear upper bound in the line metric space [21]. Next, we provide the first randomized strategy-proof mechanism with a constant approximation ratio of 4. Our mechanism works in general metric spaces. For randomized strategy-proof mechanisms, the previous best upper bound is O(n) which works only in the line metric space.
机译:我们认为在公制空间中定位设施的问题,以服务一组自私的代理商。代理的成本是她自己的位置与最近的设施之间的距离。社会成本是代理商的总成本。我们有兴趣设计策略的机制,无需支付近似值的社会成本。一种机制是一种(可能随机的)算法,其将代理报告的位置映射到设施的位置。如果没有代理商可以从任何配置中的错误重新报告她的位置,那么机制是策略的。首先是由Procaccia和Tennen-Holtz研究的这个设置[21]。他们专注于工厂游戏,在实际线上的代理和设施。 Alon等人。研究了一般公制空间中设施游戏的机制[1]。但是,他们只关注一个设施的游戏。在本文中,我们研究了一般公制空间中的两个设施游戏,它延伸了以前的模型。我们首先证明确定性战略机制的社会成本近似率的FI(n)界。我们的下限甚至持有线路公制空间。这显着改善了先前的恒定下限[21,17]。请注意,线路度量空间中存在匹配的线性上限[21]。接下来,我们提供具有恒定近似率的第一种随机策略的方法。我们的机制在一般度量空间中工作。对于随机策略的策略机制,以前的最佳上限是o(n),它仅在线公制空间工作。

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