The fractal geometry of opinion formation

TL;DR

This paper introduces a new agent-based opinion dynamics model exhibiting fractal-like equilibrium structures, derived through mean-field analysis, which enhances understanding of opinion fragmentation phenomena in large populations.

Contribution

The paper rigorously derives the mean-field PDE for a novel opinion model and reveals self-similar equilibrium distributions related to fractal geometry, extending previous opinion dynamics research.

Findings

Equilibrium opinions form fractal-like self-similar structures.

Mean-field PDE accurately describes large-population limit.

Model captures opinion fragmentation phenomena.

Abstract

In this manuscript, we introduce and study a variant of the agent-based opinion dynamics proposed in a recent work [8], within the framework of an interacting multi-agent system, where agents are assumed to interact with each other and update their opinions after each pairwise encounter. Specifically, our opinion model involves a large crowd of $N$ indistinguishable agents, each characterized by an opinion value ranging within the interval $[-1,1]$. At each update time, two agents are picked uniformly at random and the opinion of one agent will either shift by a proportion $\mu_+ \in (0,1]$ towards $+1$, or by a proportion $\mu_- \in (0,1]$ towards $-1$, with probabilities depending on the other agent's opinion. We rigorously derive the mean-field limit PDE that governs the large-population limit of the agent-based model and present several quantitative results demonstrating convergence to the unique equilibrium distribution. Remarkably, for a suitable choice of model parameters, the long-term equilibrium opinion profile displays a striking self-similar structure that generalizes the celebrated Bernoulli convolution, a topic extensively studied in the context of fractal geometry [23,44]. These findings also enhance our understanding of the opinion fragmentation phenomenon and may provide valuable insights for the development of more sophisticated models in future research.