Network Complexity of Foodwebs

TL;DR

This paper investigates the complexity of ecological networks using an information theoretic measure, comparing real food webs with artificial life models, and finds that artificial models lack the complexity surplus seen in real networks.

Contribution

It applies a complexity measure to artificial life models of ecological networks to test if they replicate the complexity observed in real food webs.

Findings

Real food webs show higher complexity than randomized controls.

Artificial life models do not exhibit the same complexity surplus.

Complexity differences may reflect evolutionary processes in real networks.

Abstract

In previous work, I have developed an information theoretic complexity measure of networks. When applied to several real world food webs, there is a distinct difference in complexity between the real food web, and randomised control networks obtained by shuffling the network links. One hypothesis is that this complexity surplus represents information captured by the evolutionary process that generated the network. In this paper, I test this idea by applying the same complexity measure to several well-known artificial life models that exhibit ecological networks: Tierra, EcoLab and Webworld. Contrary to what was found in real networks, the artificial life generated foodwebs had little information difference between itself and randomly shuffled versions.