IJCAI 2022poster4 citations

Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space

Chinmay Sonar, Subhash Suri, Jie Xue

Abstract

We consider multiwinner elections in Euclidean space using the minimax Chamberlin-Courant rule. In this setting, voters and candidates are embedded in a d-dimensional Euclidean space, and the goal is to choose a committee of k candidates so that the rank of any voter's most preferred candidate in the committee is minimized. (The problem is also equivalent to the ordinal version of the classical k-center problem.) We show that the problem is NP-hard in any dimension d >= 2, and also provably hard to approximate. Our main results are three polynomial-time approximation schemes, each of which finds a committee with provably good minimax score. In all cases, we show that our approximation bounds are tight or close to tight. We mainly focus on the 1-Borda rule but some of our results also hold for the more general r-Borda.

Agent-based and Multi-agent Systems: Computational Social ChoiceAgent-based and Multi-agent Systems: Algorithmic Game Theory
BibTeX
@inproceedings{ijcai2022p68,
  title     = {Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space},
  author    = {Sonar, Chinmay and Suri, Subhash and Xue, Jie},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {475--481},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/68},
  url       = {https://doi.org/10.24963/ijcai.2022/68},
}
Multiwinner Elections under Minimax Chamberlin-Courant Rule in Euclidean Space · IJCAI 2022