Nucleation transitions in polycontextural networks toward consensus

TL;DR

This paper analyzes phase transitions in polycontextural networks, revealing how agents' responsiveness influences the emergence of consensus and the formation of clusters with self-similar properties.

Contribution

It provides an analytical estimate of the critical point and characterizes the scaling and space-time structure of belief clusters near the transition.

Findings

Identifies a phase transition between active and frozen belief states.

Shows that clusters exhibit self-similar, scale-invariant behavior near criticality.

Demonstrates the role of susceptibility in driving consensus formation.

Abstract

Recently, we proposed polycontextural networks as a model of evolving systems of interacting beliefs. Here, we present an analysis of the phase transition as well as the scaling properties. The model contains interacting agents that strive for consensus, each with only subjective perception. Depending on a parameter that governs how responsive the agents are to changing their belief systems the model exhibits a phase transition that mediates between an active phase where the agents constantly change their beliefs and a frozen phase, where almost no changes appear. We observe the build-up of convention-aligned clusters only in the intermediate regime of diverging susceptibility. Here, we analyze in detail the behavior of polycontextural networks close to this transition. We provide an analytical estimate of the critical point and show that the scaling properties and the space-time structure of these clusters show self-similar behavior. Our results not only contribute to a better understanding of the emergence of consensus in systems of distributed beliefs but also show that polycontextural networks are models, motivated by social systems, where susceptibility -- the sensitivity to change own beliefs -- drives the growth of consensus clusters.