Digital Ecosystems: Self-Organisation of Evolving Agent Populations

TL;DR

This paper explores the self-organising behavior of Digital Ecosystems created via evolutionary computing in Multi-Agent Systems, proposing measures to quantify and analyze the emergence and efficiency of self-organisation.

Contribution

It introduces a novel measure called Physical Complexity for assessing self-organisation in evolving agent populations, including variable-length populations and clustering phenomena.

Findings

Physical Complexity effectively quantifies self-organisation.

The measure accounts for variable-length populations.

Insights into clustering and self-organisation dynamics.

Abstract

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. Self-organisation is perhaps one of the most desirable features in the systems that we engineer, and it is important for us to be able to measure self-organising behaviour. We investigate the self-organising aspects of Digital Ecosystems, created through the application of evolutionary computing to Multi-Agent Systems (MASs), aiming to determine a macroscopic variable to characterise the self-organisation of the evolving agent populations within. We study a measure for the self-organisation called Physical Complexity; based on statistical physics, automata theory, and information theory, providing a measure of information relative to the randomness in an organism's genome, by calculating the entropy in a population. We investigate an extension to include populations of variable length, and then built upon this to construct an efficiency measure to investigate clustering within evolving agent populations. Overall an insight has been achieved into where and how self-organisation occurs in our Digital Ecosystem, and how it can be quantified.