Evaluating approval-based multiwinner voting in terms of robustness to noise
Abstract
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 is always robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.
Cite
@article{arxiv.2002.01776,
title = {Evaluating approval-based multiwinner voting in terms of robustness to noise},
author = {Ioannis Caragiannis and Christos Kaklamanis and Nikos Karanikolas and George A. Krimpas},
journal= {arXiv preprint arXiv:2002.01776},
year = {2020}
}
Comments
Preliminary version appeared in IJCAI 2020