Paper Info
Title
Expected Emergence of Algorithmic Information from a Lower Bound for Stationary Prevalence.
Abstract
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 Barabu0027{a}si-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.
Year
DOI
Venue
2018
10.5281/zenodo.1241237
arXiv: Social and Information Networks
DocType
Volume
ISSN
Journal
abs/1812.05912
Brazilian Computer Society Congress 2018 (CSBC 2018), Natal, 2018. Brazilian Computer Society (SBC). Available at http://portaldeconteudo.sbc.org.br/index.php/etc/article/view/3149
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Felipe S. Abrahão101.69
Klaus Wehmuth27010.17
Artur Ziviani364656.62