Strategyproof Facility Location for Concave Cost Functions
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Author(s)
Fotakis, Dimitris
Tzamos, Christos
Date Issued
July 2015
Journal
Algorithmica
Publisher
Springer US
Citation
Fotakis, Dimitris, and Christos Tzamos. “Strategyproof Facility Location for Concave Cost Functions.” Algorithmica 76.1 (2016): 143–167.
Version
Author's final manuscript
Abstract
We consider k-Facility Location games, where n strategic agents report their locations on the real line and a mechanism maps them to k facilities. Each agent seeks to minimize his connection cost, given by a nonnegative increasing function of his distance to the nearest facility. Departing from previous work, that mostly considers the identity cost function, we are interested in mechanisms without payments that are (group) strategyproof for any given cost function, and achieve a good approximation ratio for the social cost and/or the maximum cost of the agents. We present a randomized mechanism, called Equal Cost, which is group strategyproof and achieves a bounded approximation ratio for all k and n, for any given concave cost function. The approximation ratio is at most 2 for Max Cost and at most n for Social Cost. To the best of our knowledge, this is the first mechanism with a bounded approximation ratio for instances with k ≥ 3 facilities and any number of agents. Our result implies an interesting separation between deterministic mechanisms, whose approximation ratio for Max Cost jumps from 2 to unbounded when k increases from 2 to 3, and randomized mechanisms, whose approximation ratio remains at most 2 for all k. On the negative side, we exclude the possibility of a mechanism with the properties of Equal Cost for strictly convex cost functions. We also present a randomized mechanism, called Pick the Loser, which applies to instances with k facilities and only n=k+1 agents. For any given concave cost function, Pick the Loser is strongly group strategyproof and achieves an approximation ratio of 2 for Social Cost.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1007/s00453-015-0026-6
