An Agent-based Model of Opinion Dynamics with Hierarchical Thinking

Opinion dynamics studies the principles governing the evolution of collective opinion, offering valuable insights into the comprehension of social phenomena and forecasting group behavior. However, existing opinion dynamics models often overlook the impact of both opinion climate and cognitive capacities on interactive behaviors, thus causing simulation outcomes diverge from real-world observations. Addressing this gap, we propose a novel opinion dynamics model based on hierarchical thinking to describe the opinion evolution on social networks. Individuals are classified into different levels according to their cognitive abilities. They act with bounded rationality at their respective levels to optimize both the promotion of personal opinions and the avoidance of cyberbullying. Through simulation analysis, we found the crucial role of users with high levels of hierarchical thinking. They can discern the opinion climate and articulate their opinion, acting as bridges in the evolution of public opinion. Their opinion can reach the bounded confidence range of more people, thereby enabling polarization to shift to consensus under the same conditions. Furthermore, this effect is independent of individual inherent attributes, which is more in line with real-life scenarios.