Quantitatively Interpreting Residents Happiness Prediction by Considering Factor-Factor Interactions
Quantitatively Interpreting Residents Happiness Prediction by Considering Factor-Factor Interactions
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DOI：
10.1109/tcss.2023.3246181
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发表时间：
2023-02-27
影响因子：
5
通讯作者：
Zhang, Jianwei
中科院分区：
文献类型：
--
作者：
Li, Lin;Wu, Xiaohua;Zhang, Jianwei
探索居民幸福感的高效影响因素，对各国的经济政策和政治政策的制定具有重要意义。由于社会学家的关注，以前的努力，手动预定义的多因素之间的关系，以分析高解释性的回归模型的因素之间的相互作用。最近，深度学习方法通过自动学习因素之间的加性相互作用，显示出很有前途的预测准确性，同时它也面临着可解释性的挑战。为此，一种无偏的事后和模型不可知的方法有望定量解释幸福预测模型的结果。因此，本文提出了一种基于深度神经网络（DNN）和Shapley值的新解决方案，以基于联盟博弈论计算不同联盟中的因子-因子交互作用。为了评估我们的解决方案的两两交互的可解释性质量，中国综合社会调查（CGSS）和欧洲社会调查（ESS）问卷数据集上进行了实验。通过系统评价，实验结果与社会科学学术研究高度一致。从不同的因素类别分析，新的发现是，一些因素，如，身体质量指数（BMI）和社交媒体在预测幸福感方面发挥着至关重要的作用，但在社会科学研究中很少被考虑。具体来说，在多个类别之间存在一些严重的相互作用，例如个人信息（健康和年龄）与经济（保险和收入）。因此，这一解决方案可以从理论上支持社会决策的含义。
Exploring the high-effect factors of residents' happiness is good for a wide range of policy-making for economics and politics in most countries. Limited to the concerns of sociologists, previous efforts manually predefined the relationship among multifactors to analyze the factor-factor interactions with high interpretability by regression models. Recently, deep learning methods show great promising prediction accuracy by automatically learning additive interaction between factors, while it meets the challenges of interpretability. To this end, an unbiased post-hoc and model-agnostic method is promising to quantitatively interpret the results of the happiness prediction model. Thus, this article proposes a novel solution based on the deep neural network (DNN) and the Shapley value to compute the factor-factor interactions in different coalitions based on coalitional game theory. Aiming at evaluating the pairwise interactions interpretability quality of our solution, experiments are conducted on the Chinese General Social Survey (CGSS) and European Social Survey (ESS) questionnaire datasets. By systematic reviews, the experimental results are highly consistent with academic studies in social science. Analyzed by different factor categories, the new finding is that some factors, e.g., body mass index (BMI) and social media, play a crucial role in happiness prediction but are rarely considered in social science studies. Specifically, there are some heavy interactions across multicategories, such as personal information (health and age) with economics (insurance and income). Therefore, this solution can theoretically support the implications of social decision-making.