Open AccessExpected Emergence of Algorithmic Information from a Lower Bound for Stationary Prevalence
Author(s) -
Felipe S. Abrahão,
Klaus Wehmuth,
Artur Ziviani
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/etc.2018.3149
Subject(s) - imitation , upper and lower bounds , stationary distribution , node (physics) , population , computer science , population size , complex network , preferential attachment , scale free network , scale (ratio) , mathematics , theoretical computer science , statistics , demography , combinatorics , biology , geography , physics , mathematical analysis , cartography , quantum mechanics , neuroscience , sociology , markov chain
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.