Strategy-proof Mechanisms for Facility Location Games with Many Facilities

Abstract : This paper is devoted to the location of public facilities in a metric space. Selfish agents are located in this metric space, and their aim is to minimize their own cost, which is the distance from their location to the nearest facility. A central authority has to locate the facilities in the space, but she is ignorant of the true locations of the agents. The agents will therefore report their locations, but they may lie if they have an incentive to do it. We consider two social costs in this paper: the sum of the distances of the agents to their nearest facility, or the maximal distance of an agent to her nearest facility. We are interested in designing strategy-proof mechanisms that have a small approximation ratio for the considered social cost. A mechanism is strategy-proof if no agent has an incentive to report false information. In this paper, we design strategy-proof mechanisms to locate n − 1 facilities for n agents. We study this problem in the general metric and in the tree metric spaces. We provide lower and upper bounds on the approximation ratio of deterministic and randomized strategy-proof mechanisms.
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Bruno Escoffier, Laurent Gourvès, Kim Thang Nguyen, Fanny Pascual, Olivier Spanjaard. Strategy-proof Mechanisms for Facility Location Games with Many Facilities. 2nd International Conference on Algorithmic Decision Theory (ADT'11), Oct 2011, Piscataway, NJ, United States. pp.67-81, ⟨10.1007/978-3-642-24873-3_6⟩. ⟨hal-01285708⟩

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