Autocatalytic Sets and the Growth of Complexity in an Evolutionary Model

Abstract

A model of $s$ interacting species is considered with two types of dynamical variables. The fast variables are the populations of the species and slow variables the links of a directed graph that defines the catalytic interactions among them. The graph evolves via mutations of the least fit species. Starting from a sparse random graph, we find that an autocatalytic set inevitably appears and triggers a cascade of exponentially increasing connectivity until it spans the whole graph. The connectivity subsequently saturates in a statistical steady state. The time scales for the appearance of an autocatalytic set in the graph and its growth have a power law dependence on $s$ and the catalytic probability. At the end of the growth period the network is highly nonrandom, being localized on an exponentially small region of graph space for large $s$.