On the Complexity of Winner Verification and Candidate Winner for Multiwinner Voting Rules
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 89-95. https://doi.org/10.24963/ijcai.2020/13
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.
Agent-based and Multi-agent Systems: Computational Social Choice
Agent-based and Multi-agent Systems: Voting