Emergence of complexity in opinion propagation: A reaction-diffusion model

TL;DR

This paper introduces a reaction-diffusion model inspired by social contagion to analyze how opinions evolve and become more complex within populations, revealing that larger communities can support higher opinion complexity.

Contribution

It presents a novel reaction-diffusion framework for opinion dynamics and characterizes the maximal complexity of opinions that can persist and how it scales with population size.

Findings

Maximal opinion complexity can grow almost exponentially with population size.

The model links parameters to the emergence of complex opinions.

Large communities facilitate the development of more complex opinions.

Abstract

We analyze a model designed to describe the spread and accumulation of opinions in a population. Inspired by the social contagion paradigm, our model is built on the classical SIR model of Kermack and McKendrick and consists in a system of reaction-diffusion equations. In the scenario we consider, individuals within the population can adopt new opinions via interactions with others, following some simple rules. The individuals can gradually adopt more complex opinions over time. Our main result is the characterization of a maximal complexity of opinions that can persist and propagate. In addition, we show how the parameters of the model influence this maximal complexity. Notably, we show that it grows almost exponentially with the size of the population, suggesting that large communities can foster the emergence of more complex opinions.