Hierarchical Causal Model for Analysis of Complex Social System

Currently, various rapidly developing information technologies are gradually transforming traditional social systems into Complex Social Systems (CSS). On the one hand, individuals’ ability to make decisions and access information is increasing, making their behaviors more unpredictable. On the other hand, technology is facilitating an increase in the intensity and scope of individual interactions, with cascade effects making the outcomes of interactions difficult to estimate. To improve the performance of CSS, it is essential to examine the causal laws that determine what kind of performance the system exhibits. However, researches on the causal laws of CSS remain scarce, leading to the lack of foundations for analyzing such systems. Inspired by computational experiments and causal analysis, this paper proposes a Hierarchical Causal Model (HCM) with three layers, each of which presents, extracts, and applies the causality. We apply the proposed model to enhance the system performance in a typical CSS, a software-enabled small-scale plant. Experimental results show that 98.38% of the working days have better system performance than the actual performance after applying our proposed model, and the mean of the median improvement reaches 41.38%. These results validate the proposed model, demonstrating that this work provides a feasible method for the analysis of CSS.