%0 Journal Article
%J Entropy
%D 2016
%T Measuring the Complexity of Continuous Distributions
%A Santamaría-Bonfil, Guillermo
%A Fernández, Nelson
%A Gershenson, Carlos
%X We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon's information, the novel continuous complexity measures describe how a system's predictability changes in terms of the probability distribution parameters. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.
%B Entropy
%V 18
%P 72
%G eng
%U http://www.mdpi.com/1099-4300/18/3/72
%R 10.3390/e18030072
%0 Book Section
%B Advances in Computational Biology
%D 2014
%T Measuring Complexity in an Aquatic Ecosystem
%A Fernández, Nelson
%A Gershenson, Carlos
%E Castillo, Luis F.
%E Cristancho, Marco
%E Isaza, Gustavo
%E Pinzón, Andrés
%E Corchado Rodríguez, Juan Manuel
%X We apply formal measures of emergence, self-organization, homeostasis, autopoiesis and complexity to an aquatic ecosystem; in particular to the physiochemical component of an Arctic lake. These measures are based on information theory. Variables with an homogeneous distribution have higher values of emergence, while variables with a more heterogeneous distribution have a higher self-organization. Variables with a high complexity reflect a balance between change (emergence) and regularity/order (self-organization). In addition, homeostasis values coincide with the variation of the winter and summer seasons. Autopoiesis values show a higher degree of independence of biological components over their environment. Our approach shows how the ecological dynamics can be described in terms of information.
%B Advances in Computational Biology
%S Advances in Intelligent Systems and Computing
%I Springer
%V 232
%P 83-89
%G eng
%U http://arxiv.org/abs/1305.5413
%R 10.1007/978-3-319-01568-2_12