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Evaluating approval-based multiwinner voting in terms of robustness to noise

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  • Ioannis Caragiannis
  • Christos Kaklamanis, University of Patras, Greece
  • Nikos Karanikolas, University of Patras, Greece
  • George Krimpas, University of Patras, Greece

Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting can indeed be robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.

Original languageEnglish
Article number1
JournalAutonomous Agents and Multi-Agent Systems
Publication statusPublished - Apr 2022

    Research areas

  • Approval-based voting, Computational social choice, Multiwinner voting rules, Noise models

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