Nonlinear Agent-Based Dynamics: Which Metric Mitigates Polarization?

This article provides a stochastic agent-based model to exhibit the role of aggregation metrics in order to mitigate polarization in a complex society. Our sociophysics model is based on interacting and nonlinear Brownian agents, which allow us to study the emergence of collective opinions. The opinion of an agent, $xi(t)$ is a continuous positive value in an interval [0,1]. We find: (i) most agent-metrics display similar outcomes, (ii) The middle-metric and noisy-metric obtain new opinion dynamics either toward assimilation or fragmentation, and (iii) We show that a developed two-stage metric provide new insights about convergence and equilibria. In summary, our simulation demonstrates the power of institutions, which affect the emergence of collective behavior. Consequently, opinion formation in a decentralized complex society is reliant to the individual information processing and rules of collective behavior.