Split manipulations in cost sharing of minimum cost spanning tree

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

This paper studies minimum cost spanning tree (MCST) problems, in which an agent can behave as multiple agents by adding fake accounts. Since such split manipulations may increase the cost of MCST, it is important to (i) design a cost allocation rule under which no agent has an incentive to split her accounts, and (ii) analyze the resistance of the existing cost allocation rules against split manipulations. We first show that there exists no cost allocation rule that is both efficient and split-proof under the general domain. We then focus on the MCST problems with monotonic weight functions and show that there exists a cost allocation rule that is efficient, core-selecting, and split-proof. We finally analyze the resistance of the Bird rule, one of the most studied cost allocation rules in the literature, against split manipulations from three different perspectives: The mixed price of anarchy, the computational difficulty of manipulation, and domain restrictions.

本文言語英語
ホスト出版物のタイトルECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings
編集者Giuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang
出版社IOS Press BV
ページ219-226
ページ数8
ISBN(電子版)9781643681009
DOI
出版ステータス出版済み - 8 24 2020
イベント24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Santiago de Compostela, Online, スペイン
継続期間: 8 29 20209 8 2020

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
325
ISSN(印刷版)0922-6389

会議

会議24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
国/地域スペイン
CitySantiago de Compostela, Online
Period8/29/209/8/20

All Science Journal Classification (ASJC) codes

  • 人工知能

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