Navarrete Carlos, Macedo Mariana, Colley Rachael, Zhang Jingling, Ferrada Nicole, Mello Maria Eduarda, Lira Rodrigo, Bastos-Filho Carmelo, Grandi Umberto, Lang Jérôme, Hidalgo César A
Center for Collective Learning, ANITI, TSE, IAST, IRIT, Université de Toulouse, Toulouse, France.
IRIT, Université Toulouse Capitole, Toulouse, France.
Nat Hum Behav. 2024 Jan;8(1):137-148. doi: 10.1038/s41562-023-01755-x. Epub 2023 Nov 16.
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 programmes 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 that divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.
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度。然而，数字参与活动通常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，在该实验中，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来构建个性化的政府计划。我们利用这些数据探索补充社会选择理论中所使用的聚合方法，发现一种与传统聚合函数不相关的分歧度量可以识别极化提议。这些度量为每个提议的分歧程度提供了一个分数，该分数可以在没有参与者人口特征数据的情况下进行估计，并且能够解释使民众产生分歧的问题。这些发现表明，在直接形式的数字参与中，分歧度量可以作为传统聚合函数的有用补充。
