On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules
Abstract
The Chamberlin-Courant and Monroe rules are fundamental and well-studied rules in the literature of multi-winner elections. The problem of determining if there exists a committee of size k that has a Chamberlin-Courant (respectively, Monroe) dissatisfaction score of at most r is known to be NP-complete. We consider the following natural problems in this setting: a) given a committee S of size k as input, is it an optimal k-sized committee?, and b) given a candidate c and a committee size k, does there exist an optimal k-sized committee that contains c? In this work, we resolve the complexity of both problems for the Chamberlin-Courant and Monroe voting rules in the settings of rankings as well as approval ballots. We show that verifying if a given committee is optimal is coNP-complete whilst the latter problem is complete for Theta_2^P. Our contribution fills an essential gap in the literature for these important multi-winner rules.
Cite
Text
Sonar et al. "On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/13Markdown
[Sonar et al. "On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/sonar2020ijcai-complexity/) doi:10.24963/IJCAI.2020/13BibTeX
@inproceedings{sonar2020ijcai-complexity,
title = {{On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules}},
author = {Sonar, Chinmay and Dey, Palash and Misra, Neeldhara},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {89-95},
doi = {10.24963/IJCAI.2020/13},
url = {https://mlanthology.org/ijcai/2020/sonar2020ijcai-complexity/}
}