English

More Efforts Towards Fixed-Parameter Approximability of Multiwinner Rules

Computer Science and Game Theory 2025-07-21 v2 Data Structures and Algorithms

Abstract

Multiwinner Elections have emerged as a prominent area of research with numerous practical applications. We contribute to this area by designing parameterized approximation algorithms and also resolving an open question by Yang and Wang [AAMAS'18]. More formally, given a set of candidates, \mathcal{C}, a set of voters,\mathcal{V}, approving a subset of candidates (called approval set of a voter), and an integer kk, we consider the problem of selecting a ``good'' committee using Thiele rules. This problem is computationally challenging for most Thiele rules with monotone submodular satisfaction functions, as there is no (1-\frac{1}{e}-\epsilon)\footnote{Here, ee denotes the base of the natural logarithm.}-approximation algorithm in f(k)(|\mathcal{C}| + |\mathcal{V}|)^{o(k)} time for any fixed ϵ>0\epsilon > 0 and any computable function ff, and no {\sf PTAS} even when the length of approval set is two. Skowron [WINE'16] designed an approximation scheme running in FPT time parameterized by the combined parameter, size of the approval set and kk. In this paper, we consider a parameter d+kd+k (no dd voters approve the same set of dd candidates), where dd is upper bounded by the size of the approval set (thus, can be much smaller). With respect to this parameter, we design parameterized approximation schemes, a lossy polynomial-time preprocessing method, and show that an extra committee member suffices to achieve the desired score (i.e., 11-additive approximation). Additionally, we resolve an open question by Yang and Wang~[AAMAS'18] regarding the fixed-parameter tractability of the problem under the PAV rule with the total score as the parameter, demonstrating that it admits an FPT algorithm.

Keywords

Cite

@article{arxiv.2505.12699,
  title  = {More Efforts Towards Fixed-Parameter Approximability of Multiwinner Rules},
  author = {Sushmita Gupta and Pallavi Jain and Souvik Saha and Saket Saurabh and Anannya Upasana},
  journal= {arXiv preprint arXiv:2505.12699},
  year   = {2025}
}

Comments

To appear in the Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2025)

R2 v1 2026-07-01T02:20:47.971Z