Diminishing the Perception Bias in the Working Environment Using a Network Generation-Based Framework

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.