Chen Shaoming, Yang Minghui, Lin Yuheng
International Business School, Guangzhou City University of Technology, Guangzhou, China.
Department of Mathematics, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China.
Front Psychol. 2022 Oct 31;13:1012796. doi: 10.3389/fpsyg.2022.1012796. eCollection 2022.
The main purpose of this paper is to investigate the happiness factors and assess the performance of machine learning techniques on predicting the happiness levels of European immigrants and natives. Two types of machine learning methods, Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN), are employed for analytical modeling. Our results with a total sample size of 196,724 respondents from nine rounds of the European Social Survey (ESS) indicate that the determinants of happiness for immigrants and natives are significantly inconsistent. Therefore, variables should be specifically selected to predict the happiness levels of these two different groups. The sensitivity analysis shows that satisfaction with life, subjective general health, and the highest level of education are the three most prominent determinants that contribute to the happiness of immigrants and natives. The overall accuracies of OLR and ANN baseline models are >80%. This can be further improved by building models for each individual country. The application of OLR and ANN implies that machine learning algorithms can be a useful tool for predicting happiness levels. The greater knowledge of migration and happiness will allow us to better understand the decision-making processes and construct more effective policies.
本文的主要目的是研究幸福因素，并评估机器学习技术在预测欧洲移民和本地人的幸福水平方面的表现。采用了两种机器学习方法，即有序逻辑回归（OLR）和人工神经网络（ANN）进行分析建模。我们对来自九轮欧洲社会调查（ESS）的196,724名受访者的总样本进行研究，结果表明，移民和本地人的幸福决定因素存在显著差异。因此，应专门选择变量来预测这两个不同群体的幸福水平。敏感性分析表明，对生活的满意度、主观总体健康状况和最高教育水平是促成移民和本地人幸福的三个最突出的决定因素。OLR和ANN基线模型的总体准确率均超过80%。通过为每个国家建立模型，这一准确率还可以进一步提高。OLR和ANN的应用表明，机器学习算法可以成为预测幸福水平的有用工具。对移民与幸福有更深入的了解将使我们能够更好地理解决策过程，并制定更有效的政策。
