使用2022年法国和巴西总统大选的在线参与数据来理解政治分裂
Carlos Navarrete1, Mariana Macedo1, Rachael Colley2
1Center for Collective Learning, ANITI, TSE, IAST, IRIT, Université de Toulouse, Toulouse, France.
Nature human behaviour
|November 17, 2023
概括
数字工具可以通过允许详细的政治偏好表达来增强公民参与. 新的分裂性指标识别了两极分化的政策提议,补充了数字参与平台中的传统社会选择方法.
科学领域:
- 政治科学 政治科学是指政治学.
- 计算社会科学 计算社会科学
- 社会选择理论社会选择理论
背景情况:
- 数字技术为公民参与和表达政治偏好提供了新的途径.
- 目前的数字参与方法经常使用不适合复杂政策组合的选举框架.
- 社会选择理论传统上汇总了偏好,但可能没有捕捉到政策特定的两极分化.
研究的目的:
- 探索数字公民参与的新型聚合指标.
- 通过新的分裂性指标,识别两极分化的政策建议.
- 评估分裂性指标在理解人口分裂方面的有用性.
主要方法:
- 进行了在线实验,参与者构建了个性化的政府计划.
- 利用了2022年法国和巴西总统大选的政策建议数据.
- 开发并应用分歧的指标来分析政策建议.
主要成果:
- 发现了一种新的分裂性指标,与传统的聚合函数不同.
- 这种分裂性指标成功地确定了两极分化的政策提案.
- 在不需要参与者的人口统计数据的情况下证明了指标的有效性.
结论:
- 分裂性指标在数字参与中为传统的聚合功能提供了有价值的补充.
- 这些指标可以有效地识别人口中的分裂问题.
- 这些发现支持使用分裂性指标来加强直接数字民主.
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