An axiomatic approach to location on networks
Mathematics of Operations Research
Approximate mechanism design without money
Proceedings of the 10th ACM conference on Electronic commerce
Tighter Bounds for Facility Games
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Proceedings of the 11th ACM conference on Electronic commerce
Asymptotically optimal strategy-proof mechanisms for two-facility games
Proceedings of the 11th ACM conference on Electronic commerce
Truthful assignment without money
Proceedings of the 11th ACM conference on Electronic commerce
Incentive compatible regression learning
Journal of Computer and System Sciences
Winner-imposing strategyproof mechanisms for multiple facility location games
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Strategyproof facility location for concave cost functions
Proceedings of the fourteenth ACM conference on Electronic commerce
On the power of deterministic mechanisms for facility location games
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
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We consider the problem of locating a public facility on a tree, where a set of n strategic agents report their locations and a mechanism determines, either deterministically or randomly, the location of the facility. The contribution of this paper is twofold. First, we introduce, for the first time, a general and clean family of strategyproof (SP) mechanisms for facility location on tree networks. Quite miraculously, all of the deterministic and randomized SP mechanisms that have been previously proposed can be cast as special cases of this family. Thus, the proposed mechanism unifies much of the existing literature on SP facility location problems, and simplifies its analysis. Second, we demonstrate the strength of the proposed family of mechanisms by proving new bounds on the approximation of the minimum sum of squares (miniSOS) objective on line and tree networks. For lines, we devise a randomized mechanism that gives 1.5-approximation, and show, through a subtle analysis, that no other randomized SP mechanism can provide a better approximation. For general trees, we construct a randomized mechanism that gives 1.83-approximation. This result provides a separation between deterministic and randomized mechanisms, as it is complemented by a lower bound of 2 for any deterministic mechanism. We believe that the devised family of mechanisms will prove useful in studying approximation bounds for additional objectives.