%0 Book Section
%B Proceedings of the Artificial Life Conference 2016
%D 2016
%T Complexity and Structural Properties in Scale-free Networks
%A Yesid Madrid
%A Carlos Gershenson
%A Nelson Fernández
%X We apply formal information measures of emergence, self-organization and complexity to scale-free random networks, to explore their association with structural indicators of network topology. Results show that the cumulative number of nodes and edges coincides with an increment of the self-organization and relative complexity, and a loss of the emergence and complexity. Our approach shows a complementary way of studying networks in terms of information.
%B Proceedings of the Artificial Life Conference 2016
%P 730–731
%G eng
%0 Journal Article
%J Complexity
%D 2012
%T Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales
%A Carlos Gershenson
%A Nelson Fernández
%X Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales.
%B Complexity
%V 18
%P 29-44
%G eng
%U http://dx.doi.org/10.1002/cplx.21424
%R 10.1002/cplx.21424