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

arXiv: https://arxiv.org/abs/2508.06454 · HTML: https://mlsanity.com/paper/2508.06454/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Multi-Winner Voting with Approval Preferences](https://mlsanity.com/paper/2007.01795/) (2020)
- [Robust and Verifiable Proportionality Axioms for Multiwinner Voting](https://mlsanity.com/paper/2302.01989/) (2023)
- [Proportionality and the Limits of Welfarism](https://mlsanity.com/paper/1911.11747/) (2019)
- [Properties of Multiwinner Voting Rules](https://mlsanity.com/paper/1506.02891/) (2015)
- [Eliciting Single-Peaked Preferences Using Comparison Queries](https://mlsanity.com/paper/1401.3449/) (2014)
- [Learning to Elect](https://mlsanity.com/paper/2108.02768/) (2021)
- [Generating Single Peaked Votes](https://mlsanity.com/paper/1503.02766/) (2015)
- [The Condorcet Principle for Multiwinner Elections: From Shortlisting to Proportionality](https://mlsanity.com/paper/1701.08023/) (2017)
- [Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist](https://mlsanity.com/paper/2401.14562/) (2024)
- [Learning Aggregation Rules in Participatory Budgeting: A Data-Driven Approach](https://mlsanity.com/paper/2412.01864/) (2024)

## Built on by (most cited first, 1 of 1)

- [Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment](https://mlsanity.com/paper/2602.03003/) (2026)

## Abstract (arXiv)

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.