Quantum Contagion: A Quantum-Like Approach for the Analysis of Social Contagion Dynamics with Heterogeneous Adoption Thresholds.

Ece C. Mutlu,Ozlem Ozmen Garibay

doi:10.3390/e23050538

Abstract

Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.

Highlights

We believe that our method, so-called quantum contagion, is able to portray the complexity of individuals and better model a social contagion process
Edge-based compartmental theory can model social contagion dynamics in most cases, these analyses fall short when transmission rates are close to these critical transmission probabilities
Numerical investigations carried out on regular networks (RRNs) show that the quantum social contagion model performs better than the extant classical social contagion model because it is able to model the dynamics near the critical transmission probabilities

Summary

Introduction

Understanding and better modeling contagion dynamics in complex networks play a crucial role in shedding light on the spreading mechanisms of viral diseases, microfinance activities, information, harmful emotions, and technology adoptions It gives us an opportunity to design more efficient anti-pathogen strategies during infectious disease outbreaks and grants theoretical foundations to predict collective behaviors, and even mitigate the propagation of false information in social systems. The importance of the reinforcement effect in social contagion is that the simple contagion mechanism in epidemic spreading, which assumes that even one single activated source might be sufficient for the transmission, is transformed into a more complex contagion mechanism This complexity in contagion dynamics is generally described by Markovian processes; these approaches are called threshold-driven, where the adoption occurs only in the existence of a certain fraction of neighbors who have already adopted, contrary to biological spreading. We can argue that possession of more complex dynamics than the largely examined epidemic contagion, and at the

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