Facility Location Games with Optional Preferences: A Revisit

Xingchen SHA, Shuyu BAO, Hau CHAN, Vincent CHAU, Ken C. K. FONG*, Minming LI

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-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, achieving approximation ratios of 3 and 2n+1 respectively. We complement the results by establishing lower bounds of 3/2 and n/4 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
Title of host publicationProceedings of the 39th Annual AAAI Conference on Artificial Intelligence
EditorsToby WALSH, Julie SHAH, Zico KOLTER
Pages14087-14094
Volume39
Edition13
DOIs
Publication statusPublished - 11 Apr 2025

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Funding

We gratefully acknowledge funding from Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/FDS13/E01/20), and by NSFC No.62202100. Minming Li is supported by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Project UGC/FDS11/E03/21. Hau Chan is supported by the National Institute of General Medical Sciences of the National Institutes of Health [P20GM130461], the Rural Drug Addiction Research Center at the University of Nebraska-Lincoln, and the National Science Foundation under grants IIS:RI #2302999 and IIS:RI #2414554. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

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