Entropy-Based Heuristic Approach For The Quantum-Like Generalization of Social Contagion

Social contagion modeling has recently attracted a great deal of attention from researchers due to its wide range of applications in network science, multi-agent systems, information science, and marketing. Since there are reinforcement effects in social contagion systems, it is necessary to consider the complexity of individuals in the system in order to understand this phenomenon. This complexity that stems from the heterogeneity of individuals and the uncertainty in their decision-making process caused the utilization of more complex social contagion modeling such as quantum-like approaches. Although these approaches are demonstrated to be able to portray this complexity and better model the social contagion process, the interference term in these models is hard to predict, causing their application very limited. To address this problem, we propose a belief-entropy-based heuristic approach to predict interference effect in quantum-like generalization of social contagion. Based on simulations of uncorrelated random regular networks (RRNs) using the proposed approach, we concluded that belief entropy is useful for detecting interference in quantum-like generalizations of social contagion models. These results should lead to increased use of quantum social contagion models in any application area without having to deal with calibration issues or time constraints.