Quantum-Inspired Modeling of Collective Behavior in Social Networks

Abstract

Understanding collective behavior in social networks is a central challenge in social science, data science, and computational modeling. Classical network and agent-based models often assume linear interactions and independent decision-making, yet real-world social systems exhibit nonlinear dynamics, rapid opinion shifts, polarization, and context-dependent responses that are difficult to predict. This study explores how quantum-inspired modeling frameworks can enhance the analysis of collective behavior by incorporating principles such as superposition, interference, and probabilistic state transitions. Rather than treating individual opinions as fixed, quantum-inspired approaches model social agents as existing in multiple potential cognitive or behavioral states that evolve through interaction and contextual influence. Through qualitative synthesis of literature in network science, behavioral modeling, and quantum-inspired computation, this paper examines how these frameworks capture emergent phenomena such as opinion cascades, synchronization, and abrupt phase transitions in social dynamics. The analysis highlights applications in misinformation diffusion, social polarization, collective decision-making, and adaptive coordination. The findings suggest that quantum-inspired models offer a flexible and scalable approach for representing uncertainty, ambiguity, and contextual dependence in social systems. Ultimately, this study positions quantum-inspired modeling as a promising tool for advancing the predictive understanding of collective behavior in complex social networks.