Mechanism design for one-facility location game with obnoxious effects on a line

Mei, Lili; Ye, Deshi; Zhang, Guochuan

doi:10.1016/j.tcs.2017.09.020

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…This section departs from the previous one because we consider the problem of selecting an undesirable candidate (e.g., build a garbage depot or choose a candidate to leave a group of people) [38,27,16,33,34,15]. In this case, one wants to select a candidate of maximum total distance to the agents.…”

Section: Selecting An Undesirable Candidatementioning

confidence: 99%

“…In the field of algorithmic mechanism design, the agents may misreport their preferences over the set of candidates so as maximize their individual distance to the winner(s). The authors of [16,33,34] pursue the goal of designing (group) strategyproof mechanisms 2 with the best possible approximation ratio. Recently, Chen et al [15] studied the distortion of algorithms in a setting where the location of the candidates is known but the location of every agent is private.…”

Section: Introductionmentioning

confidence: 99%

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On the distortion of single winner elections with aligned candidates

Fotakis

Gourvès²

2022

Auton Agent Multi-Agent Syst

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We study the problem of selecting a single element from a set of candidates on which a group of agents has some spatial preferences. The exact distances between agent and candidate locations are unknown but we know how agents rank the candidates from the closest to the farthest. Whether it is desirable or undesirable, the winning candidate should either minimize or maximize its aggregate distance to the agents. The goal is to understand the optimal distortion, which evaluates how good an algorithm that determines the winner based only on the agent rankings performs against the optimal solution. We give a characterization of the distortion in the case of latent Euclidean distances such that the candidates are aligned, but the agent locations are not constrained. This setting generalizes the well-studied setting where both agents and candidates are located on the real line. Our bounds on the distortion are expressed with a parameter which relates, for every agent, the distance to her best candidate to the distance to any other alternative.

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Section: Selecting An Undesirable Candidatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the distortion of single winner elections with aligned candidates

Fotakis

Gourvès²

2022

Auton Agent Multi-Agent Syst

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“…In terms of the agents' cost function with respect to the distance to the facility, besides linear functions, there are also other functions studied. For example, threshold based linear function [26], agent dependent linear function (happiness) [25] and concave function [18].…”

Section: Related Workmentioning

confidence: 99%

Strategyproof Mechanism for Two Heterogeneous Facilities with Constant Approximation Ratio

Yao

et al. 2019

Preprint

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In this paper, we study the two-facility location game on a line with optional preference where the acceptable set of facilities for each agent could be different and an agent's cost is his distance to the closest facility within his acceptable set. The objective is to minimize the total cost of all agents while achieving strategyproofness. We design a deterministic strategyproof mechanism for the problem with approximation ratio of 2.75, improving upon the earlier best ratio of n/2 + 1 [41].

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Two-Facility Location Games with a Minimum Distance Requirement on a Circle

Mei

Zhang

2021

Combinatorial Optimization and Applications

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Mechanism design for one-facility location game with obnoxious effects on a line

Cited by 4 publications

References 4 publications

On the distortion of single winner elections with aligned candidates

On the distortion of single winner elections with aligned candidates

Strategyproof Mechanism for Two Heterogeneous Facilities with Constant Approximation Ratio

Two-Facility Location Games with a Minimum Distance Requirement on a Circle

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