用真实数据评估投票规则，发现神经网络能更好避免规则漏洞。
What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting
- 基于真实偏好分布分析投票规则的失真频率
- 神经网络作为投票规则可减少87%的轴心违反
- 适合社会选择与算法设计研究者参考
委员会选举问题广泛存在于各类场景中，社会选择领域对不同多席位投票规则的性质满足情况日益关注。本文提出一种数据驱动框架，评估投票规则在多样化偏好分布下违反公理的实际频率，突破传统最坏情况分析的二元判断。通过该框架，我们分析了多席位投票规则与其公理表现之间的关系，并证明神经网络作为投票规则，在最小化公理违反方面优于传统规则。结果表明，数据驱动方法可为新型投票系统设计提供依据，支持社会选择领域的持续数据驱动研究。
原文摘要 · Abstract (English)
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.
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