利用2022年法国和巴西总统选举的在线参与数据理解政治分歧性
Understanding Political Divisiveness using Online Participation data from the 2022 French and Brazilian Presidential Elections
AI总结：
本文基于2022年法国和巴西总统选举在线政策组合实验，提出与传统聚合函数不相关的分歧性度量，以识别两极化提案并补充数字直接民主中的偏好聚合。
摘要：
数字技术可以通过促进详细政治偏好的表达来增强公民参与。然而，数字参与工作往往依赖于为涉及少数候选人的选举而优化的方法。在此，我们展示了在一项在线实验中收集的数据，实验参与者通过组合2022年法国和巴西总统选举候选人提出的政策，构建个性化的政府施政纲领。我们利用这些数据探索补充社会选择理论中所用聚合方式的聚合指标，发现一种与传统聚合函数不相关的分歧性度量能够识别引发两极分化的提案。这些指标为每项提案提供一个分歧性评分，该评分可以在缺乏参与者人口统计特征数据的情况下进行估计，并能解释使某一人群产生分裂的议题。这些发现表明，在直接形式的数字参与中，分歧性度量可以成为传统聚合函数的有益补充。
英文摘要：
Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programs by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
