机器学习在预测人类福祉中的作用
Ekaterina Oparina1, Caspar Kaiser2,3, Niccolò Gentile4
1London School of Economics, London, UK.
Scientific reports
|January 10, 2025
概括
机器学习 (ML) 算法显著提高了使用调查数据对主观幸福感的预测. 这些先进的方法还可以识别关键驱动因素和复杂的关系,如收入和与年龄相关的幸福曲线.
科学领域:
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
背景情况:
- 主观福祉数据在社会科学中越来越多地被使用.
- 现有的福利数据建模方法具有有限的预测能力.
研究的目的:
- 通过机器学习 (ML) 提高对自我报告幸福感的理解.
- 确定评估幸福感的关键驱动因素.
- 检查福利预测的功能形式,包括和U形效应.
主要方法:
- 基于树的机器学习算法的应用.
- 分析来自德国,英国和美国 (2010-2018) 的大型代表性调查数据.
- 将ML预测性能与标准建模方法进行比较.
主要成果:
- 与传统方法相比,ML算法在福利得分方面的预测性能优于传统方法.
- 通过ML识别的评估幸福感的关键驱动因素与现有文献一致.
- ML分析揭示了预测器关系中的细微差别,例如收入和年龄福利U形曲线.
结论:
- 机器学习为主观福祉研究提供了增强的预测能力.
- 机器学习为影响福祉的主要决定因素和复杂的功能形式提供了宝贵的见解.
- 这项研究使用调查数据建立了幸福预测的上限.
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