What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting
This work addresses the challenge of selecting fair and effective voting rules for committee formation in social choice, offering a practical, data-driven approach that could inform real-world applications, though it is incremental in applying machine learning to an existing problem.
The paper tackled the problem of evaluating multi-winner voting rules by proposing a data-driven framework to assess how often these rules violate axioms across various preference distributions, rather than relying on worst-case analysis. It found that neural networks can outperform traditional voting rules in minimizing axiom violations, suggesting potential for new voting system designs.
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.