统计模型能否捕捉Mamdani的成功？社会选择、排序复选投票与模型拟合，及其在2025年纽约市民主党初选中的应用
Can statistical models capture Mamdani's success? Social choice, ranked-choice voting, and model fit, with an application to the 2025 New York City Democratic Primary
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中文总结 AI 辅助
本文探究社会选择与统计建模的关联，将可理性化理论扩展至现实投票情境，发现可理性化模型对2025年纽约市民主党初选投票数据拟合差，非可理性化模型可捕捉选民偏好，为选举分析提供新视角。
中文摘要 AI 辅助
排序复选投票在选举中日益普及。社会选择理论（研究集体决策以在不同意见间达成妥协的学科）的大量文献探讨了各类选举程序的理论与实证属性。近期，由社会选择理论家和计算机科学家主导的关于可理性化的子文献，明确建立了社会选择规则与最大似然估计之间的联系。本文进一步阐明社会选择与数据统计汇总之间的关联。我们从统计视角研究可理性化，将现有结果扩展至现实投票情境，并推导模型估计与评估所需的统计细节。随后，我们将研究成果应用于2025年纽约市民主党市长初选的排序复选投票数据。我们证明，可理性化模型施加了不切实际的分布假设，因此对投票数据的拟合效果较差。此外，我们表明非可理性化模型能有效阐明异质选民偏好。本文研究表明，可理性化模型无法捕捉政治选举中偏好分布的关键特征，使分析人员无法使用统计建模通常具备的基本用途，如推断。相反，我们证明统计模型可通过仔细关注合理的数据生成机制，捕捉细致的选民偏好。
英文摘要
Ranked-choice voting is increasingly prevalent in elections. An extensive literature in social choice theory -- the study of collective decision-making with the goal of making compromises among disparate opinions -- considers theoretical and empirical properties of various election procedures. Recently, a sub-literature on rationalizability, led by social choice theorists and computer scientists, makes explicit connections between social choice rules and maximum likelihood estimation. This paper further illuminates connections between social choice and statistical summaries of data. We begin by studying rationalizability from a statistical perspective, expanding existing results to realistic voting contexts and deriving statistical details necessary for model estimation and assessment. We then apply our work to ranked-choice votes from the 2025 New York City Democratic mayoral primary election. We demonstrate how rationalizing models impose unrealistic distributional assumptions and thus exhibit poor fit to voting data. Additionally, we show that non-rationalizing models meaningfully elucidate heterogeneous voter preferences. Our work demonstrates the inability of rationalizing models to capture key features of distributions of preferences in political elections, depriving analysts of fundamental uses, such as inference, typically associated with statistical modeling. Conversely, we show that statistical modeling can capture nuanced voter preferences by paying careful attention to plausible data-generating mechanisms.
