Expected Emergence of Algorithmic Information from a Lower Bound for Stationary Prevalence

We study emergent information in populations of randomly generated networked computable systems that follow a susceptible-infected-susceptible contagion (or infection) model of imitation of the fittest neighbor. These networks have a scale-free degree distribution in the form of a power-law following the Barabási-Albert model. We show that there is a lower bound for the stationary prevalence (or average density of infected nodes) that triggers an unlimited increase of the expected emergent algorithmic complexity (or information) of a node as the population size grows.