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.

Income inequality is widespread in human society and has important implications for human behavior. People’s perception of the environment bridges income inequality and individual behavioral decisions. Existing research suggests that social income inequality is usually biased, either overestimated or underestimated. However, such phenomena have not been fully explored with quantitive prove, especially in the working environment based on performance data of actual production. In fact, the correct perception of people is the basis of a fair environment for their production decisions. Thus, the perception bias may weaken the adaptability and competitiveness of a company in the market. This paper first confirms the prevalence of individual perception bias of income inequality within the working environment based on actual production data. Our results show that people tend to underestimate income inequity around them, and this underestimation grows with the real unfairness of the working environment. Further, this paper proposes a network generation-based framework with a three-layer structure to correct perception bias using a cooperative network reengineering approach. Within the framework, a homophily-based generative network model is proposed as the key algorithm. Our simulation results show that our proposed framework effectively reduces individuals’ perception bias of income inequality.

E-commerce has dramatically reduced the limitation of space and time on economic activities, resulting in individuals having access to a huge number of consumers. In this paper, we propose a company’s optimal size decision model containing management costs as a means of investigating the evolution of the company size in e-commerce. Given that production decisions are made based on accessible market capacity, we explain how a company enters the market, and we draw an evolutionary path of the optimal company size. The results show that in the early expansion stage of accessible market capacity, a firm’s optimal size keeps increasing; after reaching a peak, the change in a firm’s optimal size depends on its cost management. When the accessible market capacity reaches a threshold, the firm will no longer be in the market, and may no longer exist. Finally, we construct a simulation framework based on complex adaptive systems to validate our proposed model. A simulation experiment confirms our model and reveals the dynamic co-evolution process of individual producers and firms.