Facility location with variable and dynamic populations

Yuho Wada, Tomohiro Ono, Taiki Todo, Makoto Yokoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Facility location is a well-studied problem in social choice literature. where agents' preferences are restricted to be single-peaked. When the number of agents is treated as a variable (e.g., not observable a priori), a social choice function must be defined so that it can accept any possible number of preferences as input. Furthermore, there exist cases where multiple choices must be made continuously while agents dynamically arrive/leave. Under such variable and dynamic populations, a social choice function needs to give each agent an incentive to sincerely report her existence. In this paper we investigate facility location models with variable and dynamic populations. For a static, i.e., one-shot, variable population model, we provide a necessary and sufficient condition for a social choice function to satisfy participation, as well as truthfulness, anonymity, and Pareto efficiency. The condition is given as a further restriction on the well-known median voter schemes. For a dynamic model, we first propose an online social choice function, which is optimal for the total sum of the distances between the choices in the previous and current periods, among any Pareto efficient functions. We then define a generalized class of online social choice functions and compare their performances both theoretically and experimentally.

Original languageEnglish
Title of host publication17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages336-344
Number of pages9
ISBN (Print)9781510868083
Publication statusPublished - Jan 1 2018
Event17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 - Stockholm, Sweden
Duration: Jul 10 2018Jul 15 2018

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume1
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Other

Other17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
CountrySweden
CityStockholm
Period7/10/187/15/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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