False-name-proof facility location on discrete structures

Taiki Todo, Nodoka Okada, Makoto Yokoo

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

3 Citations (Scopus)

Abstract

We consider the problem of locating a single facility on a vertex in a given graph based on agents' preferences, where the domain of the preferences is either single-peaked or single-dipped, depending on whether they want to access the facility (a public good) or be far from it (a public bad). Our main interest is the existence of deterministic social choice functions that are Pareto efficient and false-name-proof, i.e., resistant to fake votes. We show that regardless of whether preferences are single-peaked or single-dipped, such a social choice function exists (i) for any tree graph, and (ii) for a cycle graph if and only if its length is less than six. We also show that when the preferences are single-peaked, such a social choice function exists for any ladder (i.e., 2 × m grid) graph, and does not exist for any larger (hyper)grid.

Original languageEnglish
Title of host publicationECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings
EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang
PublisherIOS Press BV
Pages227-234
Number of pages8
ISBN (Electronic)9781643681009
DOIs
Publication statusPublished - Aug 24 2020
Event24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Santiago de Compostela, Online, Spain
Duration: Aug 29 2020Sept 8 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume325
ISSN (Print)0922-6389

Conference

Conference24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
Country/TerritorySpain
CitySantiago de Compostela, Online
Period8/29/209/8/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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