中国城市的收入,心理安全和主观幸福感:用SHAP解释的机器学习分析
Juan Song1, Feilong Pan2, Haiyan Liu3
1School of Economics and Management, China University of Geosciences Beijing, Haidian District, No. 29, College Road, Beijing, People's Republic of China.
BMC psychology
|November 26, 2025
概括
心理安全是主观幸福的关键,比单单收入更重要. 机器学习模型揭示了复杂的关系,为有针对性的福祉政策提供了洞察力.
科学领域:
- 社会科学 社会科学 社会科学
- 心理学 心理学 心理学
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
背景情况:
- 主观幸福感是社会进步的关键指标,但伊斯特林悖论强调了经济增长和幸福感之间的脱节.
- 传统的经济指标,如收入,对幸福感的影响越来越小.
- 心理安全的多维构造及其对幸福感的影响仍然未被充分探索,特别是其非线性关系.
研究的目的:
- 使用先进的机器学习技术,研究心理安全,收入和主观幸福之间的复杂,非线性关系.
- 确定主观幸福的主要驱动因素,并阐明其在不同社会经济群体形成的基础机制.
- 克服传统线性模型在捕捉影响幸福感的复杂变量相互作用方面的局限性.
主要方法:
- 在一项针对1369名中国城市居民的全国性调查中使用机器学习算法 (LightGBM,XGBoost,Random Forest).
- 使用SHapley添加式解释 (SHAP) 进行解释,以分析变量的重要性和影响.
- 实施数据预处理技术,包括归算,标准化和SMOTE-Tomek采样,通过交叉验证和网格搜索进行模型优化.
主要成果:
- 轻GBM模型实现了最高的性能 (AUC=0.854),超过了传统的线性模型.
- 心理安全成为影响所有收入水平主观幸福感的最关键因素.
- 收入表现出值效应,中等收入水平显示出最强的积极影响,心理安全显著放大.
结论:
- 心理安全是主观幸福的最稳定和核心决定因素,与收入有显著的协同效应.
- 机器学习和SHAP分析有效地捕获复杂的非线性,并提供对幸福机制的可解释的见解.
- 研究结果支持差异化政策干预,强调心理安全和资源匹配策略,针对不同福利群体.
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