%0 Journal Article
%J Frontiers in Robotics and AI
%D 2017
%T A Package for Measuring Emergence, Self-organization, and Complexity Based on Shannon Entropy
%A Santamaría-Bonfil, Guillermo
%A Gershenson, Carlos
%A Fernández, Nelson
%X We present Matlab/Octave functions to calculate measures of emergence, self-organization, and complexity of discrete and continuous data. The measures are based on Shannon's information and differential entropy, respectively. Examples from different datasets and probability distributions are used to illustrate the usage of the code.
%B Frontiers in Robotics and AI
%V 4
%P 10
%G eng
%U http://journal.frontiersin.org/article/10.3389/frobt.2017.00010
%R 10.3389/frobt.2017.00010
%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