Modeling pattern formation in communities by using information particles

TL;DR

This paper introduces an innovative information-particle model inspired by reaction-diffusion systems to simulate community pattern formation, capturing complex dynamics and transitions solely based on system parameters.

Contribution

The novel information-particle model combines reaction-diffusion and distributed behavior concepts to reproduce diverse community patterns and transitions, including the elusive competitive equilibrium.

Findings

Successfully models four pattern classes: stationary, competitive-equilibrium, chaotic, periodic

Reproduces complex community dynamics not captured by traditional models

Pattern transitions depend only on system parameters like number of species and their connections

Abstract

Understanding the pattern formation in communities has been at the center of attention in various fields. Here we introduce a novel model, called an "information-particle model," which is based on the reaction-diffusion model and the distributed behavior model. The information particle drives competition or coordination among species. Therefore, a traverse of information particles in a social system makes it possible to express four different classes of patterns (i.e. "stationary", "competitive-equilibrium", "chaotic", and "periodic"). Remarkably, "competitive equilibrium" well expresses the complex dynamics that is equilibrium macroscopically and non-equilibrium microscopically. Although it is a fundamental phenomenon in pattern formation in nature, it has not been obtained by conventional models. Furthermore, the pattern transitions across the classes depending only on parameters of system, namely, the number of species (vertices in network) and distance (edges) between species. It means that one information-particle model successfully develops the patterns with an in-situ computation under various environments.