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

2025-08-08

AI Summary

This study addresses the frequency with which multi-winner voting rules violate axiomatic properties—such as proportional representation and monotonicity—under realistic preference distributions, moving beyond traditional worst-case binary analysis. We propose a data-driven evaluation framework that systematically simulates mainstream rules across diverse preference distributions, including real-world elections and synthetic datasets. Crucially, we introduce neural networks as learnable voting rules, trained end-to-end to minimize axiom violations. Our experiments reveal: (1) violation rates of classical rules are highly sensitive to preference structure, and worst-case analysis substantially overestimates their deficiencies; (2) neural voting rules consistently outperform conventional methods across multiple axiomatic constraints, while retaining interpretability and strong generalization. This work establishes a more precise, empirically grounded paradigm for evaluating and designing committee election mechanisms.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Humans and AI: Voting
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Analyze multi-winner voting rules' axiom violations frequency
• Compare traditional and neural network voting rules performance
• Propose data-driven framework for voting system design

Innovation

• Data-driven framework for voting rule evaluation
• Neural networks outperform traditional voting rules
• Analyzes multi-winner rules under diverse preferences