Analyzing the Phase Transition Dynamics in Social Contagion by an Agent-Based Inverse Simulation

Abstract

This paper attempts to clarify the critical factors that determine the phase transition dynamics occurring in social contagion by an agent-based inverse simulation equipped with genetic algorithm. Previous studies have already shown that structural features such as network structure or social norm, affect significantly the macroscopic dynamics of complex social phenomena. We still need, however, a quantitative analysis of the decisive factors which could yield some particular phase transition in a society, e.g. the emergence of an epidemic. Thus we explore, by utilizing genetic algorithm, the vast parameter space of an agent-based model that represents social contagion, so that we might identify the specific values of parameters resulting in phase transitions. With the results, we will seek to describe their implications for preventing the contagion.