The Proportional Veto Principle for Approval Ballots
Authors: Daniel Halpern, Ariel D. Procaccia, Warut Suksompong
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a version of this principle for approval ballots, which we call flexible-voter representation (FVR). We show that while the approval voting rule and other natural scoring rules provide the optimal FVR guarantee only for some flexibility threshold, there exists a scoring rule that is FVR-optimal for all thresholds simultaneously. We also extend our results to multi-winner voting. The paper also includes numerous theorems and proofs, such as "Theorem 2.2. For any rule R and any s (0, 1), we have FVR(R, s) ≥ 1 − s. Moreover, for each s (0, 1), there exists a rule R such that FVR(R, s) = 1 − s." |
| Researcher Affiliation | Academia | 1Harvard University 2National University of Singapore |
| Pseudocode | Yes | Algorithm 1 (k, t) Sequential Algorithm C for j = 1, . . . , k do for i = 1, . . . , n such that Ai \ C = ∅ do wi h(m j − 1, |Ai\C| − 1, k − j; t − 1 |Ai ∩ C|) H(m, |Ai|, k; t − 1) Let aj ∈ M \C be a candidate maximizing P i∈Na wi, breaking ties arbitrarily. C ← C ∣ {aj} return C |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | This paper is theoretical and focuses on mathematical proofs and algorithm design for voting rules. It does not conduct experiments on datasets, therefore no specific access information for open datasets is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets. Therefore, no information about training/test/validation dataset splits is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe experimental results or computational evaluations that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not detail any implementation of algorithms that would require specific ancillary software or library versions for replication. |
| Experiment Setup | No | This paper is theoretical and does not contain empirical experiments. Therefore, there are no specific experimental setup details, hyperparameter values, or training configurations provided. |