Autocatalytic Sets and the Growth of Complexity in an Evolutionary Model

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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 (ACS) 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 ACS 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 non-random, being localized on an exponentially small region of graph space for large s.