What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting

• URL: http://arxiv.org/abs/2508.06454v1
• Date: Fri, 08 Aug 2025 16:54:09 GMT
• Title: What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting
• Authors: Joshua Caiata, Ben Armstrong, Kate Larson,
• Abstract summary: We propose a data-driven framework to evaluate how frequently voting rules violate axioms across diverse preference distributions.<n>We show that neural networks, acting as voting rules, can outperform traditional rules in minimizing axiom violations.
• Score: 5.880273374889066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Committee-selection problems arise in many contexts and applications, and there has been increasing interest within the social choice research community on identifying which properties are satisfied by different multi-winner voting rules. In this work, we propose a data-driven framework to evaluate how frequently voting rules violate axioms across diverse preference distributions in practice, shifting away from the binary perspective of axiom satisfaction given by worst-case analysis. Using this framework, we analyze the relationship between multi-winner voting rules and their axiomatic performance under several preference distributions. We then show that neural networks, acting as voting rules, can outperform traditional rules in minimizing axiom violations. Our results suggest that data-driven approaches to social choice can inform the design of new voting systems and support the continuation of data-driven research in social choice.

Related papers

• Dependence-Aware Label Aggregation for LLM-as-a-Judge via Ising Models [55.94503936470247]
  Large-scale AI evaluation increasingly relies on aggregating binary judgments from $K$ annotators, including judges.<n>Most classical methods assume annotators are conditionally independent given the true label $Yin0,1$, an assumption often violated by LLM judges.<n>We study label aggregation through a hierarchy of dependence-aware models based on Ising graphical models and latent factors.
  arXiv  Detail & Related papers  (2026-01-29T21:26:50Z)
• Optimized Distortion in Linear Social Choice [28.227695590829086]
  We study distortion of linear social choice for deterministic and randomized voting rules.<n>We introduce poly-time instance-optimal algorithms for minimizing distortion given a collection of candidates and votes.
  arXiv  Detail & Related papers  (2025-10-22T20:42:49Z)
• Optimal bounds for dissatisfaction in perpetual voting [84.02572742131521]
  We consider a perpetual approval voting method that guarantees that no voter is dissatisfied too many times.<n>We identify a sufficient condition on voter behavior under which a sublinear growth of dissatisfaction is possible.<n>We present a voting method with sublinear guarantees on dissatisfaction under bounded conflicts, based on the standard techniques from prediction with expert advice.
  arXiv  Detail & Related papers  (2024-12-20T19:58:55Z)
• Learning How to Vote With Principles: Axiomatic Insights Into the Collective Decisions of Neural Networks [4.8342038441006805]
  We propose axiomatic deep voting: a framework to build and evaluate neural networks that aggregate preferences. Our findings are: (1) Neural networks, despite being highly accurate, often fail to align with the core axioms of voting rules, revealing a disconnect between mimicking outcomes and reasoning. By solely optimizing axiom satisfaction, neural networks can synthesize new voting rules that often surpass and substantially differ from existing ones.
  arXiv  Detail & Related papers  (2024-10-21T16:35:58Z)
• DeepVoting: Learning Voting Rules with Tailored Embeddings [13.037431161285971]
  We recast the problem of designing a good voting rule into one of learning probabilistic versions of voting rules. We show that embeddings of preference profiles derived from the social choice literature allows us to learn existing voting rules more efficiently. We also show that rules learned using embeddings can be tweaked to create novel voting rules with improved axiomatic properties.
  arXiv  Detail & Related papers  (2024-08-24T17:15:20Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Selecting the Most Conflicting Pair of Candidates [6.838139628413773]
  We study committee elections from a perspective of finding the most conflicting candidates, that is, candidates that imply the largest amount of conflict, as per voter preferences. By proposing basic axioms to capture this objective, we show that none of the prominent multiwinner voting rules meet them. A subsequent deepened analysis sheds more light on how they operate.
  arXiv  Detail & Related papers  (2024-05-09T16:00:20Z)
• Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections [57.67176250198289]
  AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates.
  arXiv  Detail & Related papers  (2024-02-18T10:13:01Z)
• Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE [61.872917066847855]
  Methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. We develop an RLA method that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available.
  arXiv  Detail & Related papers  (2023-07-20T15:55:34Z)
• Learning to Elect [7.893831644671976]
  Voting systems have a wide range of applications including recommender systems, web search, product design and elections. We show that set-input neural network architectures such as Set Transformers, fully-connected graph networks and DeepSets are both theoretically and empirically well-suited for learning voting rules.
  arXiv  Detail & Related papers  (2021-08-05T17:55:46Z)
• Bribery as a Measure of Candidate Success: Complexity Results for Approval-Based Multiwinner Rules [58.8640284079665]
  We study the problem of bribery in multiwinner elections, for the case where the voters cast approval ballots (i.e., sets of candidates they approve) We consider a number of approval-based multiwinner rules (AV, SAV, GAV, RAV, approval-based Chamberlin--Courant, and PAV) In general, our problems tend to be easier when we limit out bribery actions on increasing the number of approvals of the candidate that we want to be in a winning committee.
  arXiv  Detail & Related papers  (2021-04-19T08:26:40Z)
• Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes [16.764511357821043]
  How should one combine noisy information from diverse sources to make an inference about an objective ground truth? We propose a multi-arm bandit noise model and count-based auxiliary information set. We find that our rules successfully use auxiliary information to outperform the naive baselines.
  arXiv  Detail & Related papers  (2020-01-27T21:21:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.