Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks

TL;DR

This paper introduces a hierarchical adaptive Random Boolean Network model that simulates complex systems by allowing coevolution of information processing and network topology within interconnected subnetworks.

Contribution

It presents a novel hierarchical adaptive RBN framework with coevolving information measures and topology, capturing the dynamics of complex hierarchical systems.

Findings

HARBN reaches steady states specific to network structure

Information measures and topology coevolve in HARBN

Model effectively describes complex hierarchical systems

Abstract

Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.