TY - JOUR
T1 - A Package for Measuring Emergence, Self-organization, and Complexity Based on Shannon Entropy
JF - Frontiers in Robotics and AI
Y1 - 2017
A1 - Santamaría-Bonfil, Guillermo
A1 - Gershenson, Carlos
A1 - Fernández, Nelson
AB - 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.
VL - 4
UR - http://journal.frontiersin.org/article/10.3389/frobt.2017.00010
ER -
TY - JOUR
T1 - Measuring the Complexity of Continuous Distributions
JF - Entropy
Y1 - 2016
A1 - Santamaría-Bonfil, Guillermo
A1 - Fernández, Nelson
A1 - Gershenson, Carlos
AB - 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.
VL - 18
UR - http://www.mdpi.com/1099-4300/18/3/72
ER -