A Quantitative Analysis of Multi-Winner Rules
Abstract
To choose a suitable multi-winner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an "optimal" subset.In this paper, we offer a new perspective on measuring the quality of such subsets and---consequently---of multi-winner rules. We provide a quantitative analysis using methods from the theory of approximation algorithms and estimate how well multi-winner rules approximate two extreme objectives: diversity as captured by the Approval Chamberlin--Courant rule and individual excellence as captured by Multi-winner Approval Voting. With both theoretical and experimental methods we classify multi-winner rules in terms of their quantitative alignment with these two opposing objectives.
Cite
Text
Lackner and Skowron. "A Quantitative Analysis of Multi-Winner Rules." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/58Markdown
[Lackner and Skowron. "A Quantitative Analysis of Multi-Winner Rules." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/lackner2019ijcai-quantitative/) doi:10.24963/IJCAI.2019/58BibTeX
@inproceedings{lackner2019ijcai-quantitative,
title = {{A Quantitative Analysis of Multi-Winner Rules}},
author = {Lackner, Martin and Skowron, Piotr},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {407-413},
doi = {10.24963/IJCAI.2019/58},
url = {https://mlanthology.org/ijcai/2019/lackner2019ijcai-quantitative/}
}