ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2412.11049
65
0
v1v2 (latest)

Distributed Facility Location Games with Candidate Locations

3 January 2025
Feiyue Sun
ArXiv (abs)PDFHTML
Abstract

We study the distributed facility location games with candidate locations, where agents on a line are partitioned into groups. Both desirable and obnoxious facility location settings are discussed. In distributed location problems, distortion can serve as a standard for quantifying performance, measuring the degree of difference between the actual location plan and the ideal location plan. For the desirable setting, under the max of sum cost objective, we give a strategyproof distributed mechanism with 555-distortion, and prove that no strategyproof mechanism can have a distortion better than 2+1\sqrt{2}+12​+1. Under the sum of max cost objective, we give a strategyproof distributed mechanism with 555-distortion, and prove that no strategyproof mechanism can have a distortion better than 5+12\frac{\sqrt{5}+1}{2}25​+1​. Under the max of max cost, we get a strategyproof distributed mechanism with 333-distortion, and prove that no strategyproof mechanism can have a distortion better than 5+12\frac{\sqrt{5}+1}{2}25​+1​. For the obnoxious setting, under three social objectives, we present that there is no strategyproof mechanism with bounded distortion in the case of discrete candidate locations, and no group strategyproof mechanism with bounded distortion in the case of continuous candidate locations.

View on arXiv
Comments on this paper