Multiwinner Temporal Voting with Aversion to Change

Valentin Zech*, Niclas Boehmer, Edith Elkind, Nicholas Teh

*Corresponding author for this work

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

Abstract

We study two-stage committee elections where voters have dynamic preferences over candidates; at each stage, a committee is chosen under a given voting rule. We are interested in identifying a winning committee for the second stage that overlaps as much as possible with the first-stage committee. We show a full complexity dichotomy for the class of Thiele rules: this problem is tractable for Approval Voting (AV) and hard for all other Thiele rules (including, in particular, Proportional Approval Voting and the Chamberlin-Courant rule). We extend this dichotomy to the greedy variants of Thiele rules. We also explore this problem from a parameterized complexity perspective for several natural parameters. We complement the theory with experimental analysis: e.g., we investigate the average number of changes in the committee as a function of changes in voters’ preferences and the role of ties.

Original languageEnglish (US)
Title of host publicationECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings
EditorsUlle Endriss, Francisco S. Melo, Kerstin Bach, Alberto Bugarin-Diz, Jose M. Alonso-Moral, Senen Barro, Fredrik Heintz
PublisherIOS Press BV
Pages3236-3243
Number of pages8
ISBN (Electronic)9781643685489
DOIs
StatePublished - Oct 16 2024
Event27th European Conference on Artificial Intelligence, ECAI 2024 - Santiago de Compostela, Spain
Duration: Oct 19 2024Oct 24 2024

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume392
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference27th European Conference on Artificial Intelligence, ECAI 2024
Country/TerritorySpain
CitySantiago de Compostela
Period10/19/2410/24/24

Funding

NB was supported by the DFG project ComSoc-MPMS (NI 369/22). EE was supported by the AI Programme of The Alan Turing Institute and an EPSRC Grant EP/X038548/1.

ASJC Scopus subject areas

  • Artificial Intelligence

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