通过机器学习和开放数据预测党派切换
Nicolò Meneghetti1,2, Fabio Pacini3,4, Francesca Biondi Dal Monte3
1The Biorobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy.
iScience
|July 7, 2023
概括
现在可以使用机器学习和开放的议会数据来预测政治党派的转换. 一个算法在预测党派切换时达到70%以上的准确性,提前两个月.
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
- 数据科学数据科学数据科学
背景情况:
- 议会动态可能是不可预测的,影响政策设计.
- 预测投票模式需要先进的分析工具和数据.
- 开放数据和机器学习为立法分析提供了潜在的解决方案.
研究的目的:
- 开发和验证用于预测意大利议会党派转换的机器学习算法.
- 分析立法投票数据,以确定党派转换之前的模式.
- 证明开放政治数据和ML在理解政治动态方面的实用性.
主要方法:
- 利用了意大利第十七届 (2013-2018) 和第十八届 (2018-2022) 的投票数据.
- 开发了一个基于机器学习技术的预测算法.
- 分析了投票行为,重点关注参与秘密投票和党派投票的连贯性.
主要成果:
- 该算法在预测党派交换方面取得了超过70%的准确性,提前两个月.
- 换党者在秘密投票中表现出更高的参与率.
- 在转换者中观察到,在他们叛逃之前,与党的多数票一致性逐渐下降.
结论:
- 机器学习与开放的政治数据相结合,可以有效地预测政党交换.
- 了解转换前的投票行为,可以了解政治动态.
- 这种方法可以通过场景模拟来支持基于证据的政策设计.
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