Quantitatively Interpreting Residents Happiness Prediction by Considering Factor–Factor Interactions

Abstract

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.