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Facility Location Games with Optional Preferences: A Revisit

  • Xingchen Sha
  • , Shuyu Bao
  • , Hau Chan
  • , Vincent Chau
  • , Ken C.K. Fong
  • , Minming Li
  • City University of Hong Kong
  • University of Nebraska-Lincoln
  • Southeast University, Nanjing
  • Lingnan University

Research output: Contribution to journalConference articlepeer-review

Abstract

We study the k-facility location games with optional preferences on the line. In the games, each strategic agent has a public location preference on the k facility locations and a private optional preference on the preferred/acceptable set of facilities out of the k facilities. Our goal is to design strategyproof mechanisms to elicit agents’ optional preferences and locate k facilities to minimize the social or maximum cost of agents based on their facility preferences and public agent locations. We consider two variants of the facility location games with optional preferences: the Min variant and the Max variant where the agent’s cost is defined as their distance to the closest acceptable facility and the farthest acceptable facility, respectively. For the Min variant, we present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost with k ≥ 3 facilities and well-separated n agents, achieving approximation ratios of 3 and 2n + 1 respectively. We complement the results by establishing lower bounds of 23 and n4 for the approximation ratios achievable by any deterministic strategyproof mechanisms for the maximum cost and social cost, respectively. We then improve our results in a special setting of the Min variant where there are exactly three facilities and present two deterministic strategyproof mechanisms to minimize the maximum cost and social cost. For the Max variant, we present an optimal deterministic strategyproof mechanism for the maximum cost and a k-approximation deterministic strategyproof mechanism for the social cost.

Original languageEnglish
Pages (from-to)14087-14094
Number of pages8
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume39
Issue number13
DOIs
StatePublished - 11 Apr 2025
Externally publishedYes
Event39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025

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