Maintaining Proportional Committees with Dynamic Candidate Sets
Authors: Chris Dong, Jannik Peters
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We extend the study of proportionality in multiwinner voting to dynamic settings, allowing candidates to join or leave the election and demanding that each chosen committee satisfies proportionality without differing too much from the previously selected committee. We consider approval preferences, ranked preferences, and the proportional clustering setting. In these settings, we either provide algorithms making few changes or show that such algorithms cannot exist for various proportionality axioms. In particular, we show that such algorithms cannot exist for ranked preferences and provide amortized and exact algorithms for several proportionality notions in the other two settings. |
| Researcher Affiliation | Academia | 1TU Munich 2National University of Singapore. Correspondence to: Chris Dong <EMAIL>, Jannik Peters <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Greedy Justified Candidate Rule (GJCR) (Brill and Peters, 2023) W for ℓin k, . . . , 1 do while there is c / W: |{i Nc : |Ai W| < ℓ}| ℓn Add candidate c maximizing |{i Nc : |Ai W| < ℓ}| to W end end return W |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | No | The paper discusses theoretical models and concepts related to multiwinner voting and proportional clustering. It does not mention the use of any specific, named publicly available datasets or provide access information (e.g., links, DOIs, citations to dataset papers) for any dataset. |
| Dataset Splits | No | The paper describes theoretical algorithms and impossibility results for dynamic multiwinner voting. It does not involve experimental evaluation on datasets, and therefore, no information regarding training/test/validation splits is provided. |
| Hardware Specification | No | The paper focuses on theoretical contributions, including algorithm design and impossibility proofs. It does not describe any experimental setup or specify hardware used for computations. |
| Software Dependencies | No | The paper focuses on theoretical contributions and algorithm design. It does not describe the implementation of these algorithms or list any specific software dependencies with version numbers that would be required to reproduce experimental results. |
| Experiment Setup | No | The paper presents theoretical work on algorithms and impossibility results for dynamic multiwinner voting. It does not describe any experimental evaluations, hyperparameters, or training configurations. |