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 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.
Cite
Text
Caragiannis et al. "Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/11Markdown
[Caragiannis et al. "Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/caragiannis2020ijcai-evaluating/) doi:10.24963/IJCAI.2020/11BibTeX
@inproceedings{caragiannis2020ijcai-evaluating,
title = {{Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise}},
author = {Caragiannis, Ioannis and Kaklamanis, Christos and Karanikolas, Nikos and Krimpas, George A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {74-80},
doi = {10.24963/IJCAI.2020/11},
url = {https://mlanthology.org/ijcai/2020/caragiannis2020ijcai-evaluating/}
}