Multi-Winner Reconfiguration

Abstract

We introduce a multi-winner reconfiguration model to examine how to transition between subsets of alternatives (aka. committees) through a sequence of minor yet impactful modifications, called reconfiguration path. We analyze this model under four approval-based voting rules: Chamberlin-Courant (CC), Proportional Approval Voting (PAV), Approval Voting (AV), and Satisfaction Approval Voting (SAV). The problem exhibits computational intractability for CC and PAV, and polynomial solvability for AV and SAV. We provide a detailed multivariate complexity analysis for CC and PAV, demonstrating that although the problem remains challenging in many scenarios, there are specific cases that allow for efficient parameterized algorithms.

Cite

Text

Chen et al. "Multi-Winner Reconfiguration." Neural Information Processing Systems, 2024. doi:10.52202/079017-2728

Markdown

[Chen et al. "Multi-Winner Reconfiguration." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/chen2024neurips-multiwinner/) doi:10.52202/079017-2728

BibTeX

@inproceedings{chen2024neurips-multiwinner,
  title     = {{Multi-Winner Reconfiguration}},
  author    = {Chen, Jiehua and Hatschka, Christian and Simola, Sofia},
  booktitle = {Neural Information Processing Systems},
  year      = {2024},
  doi       = {10.52202/079017-2728},
  url       = {https://mlanthology.org/neurips/2024/chen2024neurips-multiwinner/}
}