# Efficient Algorithms for Electing Successive Committees

arXiv: https://arxiv.org/abs/2505.18287 · HTML: https://mlsanity.com/paper/2505.18287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 9 of 9)

- [Multi-Winner Voting with Approval Preferences](https://mlsanity.com/paper/2007.01795/) (2020)
- [Proportional Aggregation of Preferences for Sequential Decision Making](https://mlsanity.com/paper/2306.14858/) (2023)
- [On the Computation of Fully Proportional Representation](https://mlsanity.com/paper/1402.0580/) (2014)
- [Temporal Fairness in Multiwinner Voting](https://mlsanity.com/paper/2312.04417/) (2023)
- [When Votes Change and Committees Should (Not)](https://mlsanity.com/paper/2005.02300/) (2020)
- [Guide to Numerical Experiments on Elections in Computational Social Choice](https://mlsanity.com/paper/2402.11765/) (2024)
- [Parameterized Algorithms for Diverse Multistage Problems](https://mlsanity.com/paper/2105.04856/) (2021)
- [Perpetual Voting: The Axiomatic Lens](https://mlsanity.com/paper/2104.15058/) (2021)
- [Parameterized Intractability for Multi-Winner Election under the Chamberlin-Courant Rule and the Monroe Rule](https://mlsanity.com/paper/2202.12006/) (2022)

## Abstract (arXiv)

In a recently introduced model of successive committee elections (Bredereck et al., AAAI-20) for a given set of ordinal or approval preferences one aims to find a sequence of a given length of "best" same-size committees such that each candidate is a member of a limited number of consecutive committees. However, the practical usability of this model remains limited, as the described task turns out to be NP-hard for most selection criteria already for seeking committees of size three. Non-trivial or somewhat efficient algorithms for these cases are lacking too. Motivated by a desire to unlock the full potential of the described temporal model of committee elections, we devise (parameterized) algorithms that effectively solve the mentioned hard cases in realistic scenarios of a moderate number of candidates or of a limited time horizon.