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 -
TY - CHAP
T1 - Measuring Complexity in an Aquatic Ecosystem
T2 - Advances in Computational Biology
Y1 - 2014
A1 - Fernández, Nelson
A1 - Gershenson, Carlos
ED - Castillo, Luis F.
ED - Cristancho, Marco
ED - Isaza, Gustavo
ED - Pinzón, Andrés
ED - Corchado Rodríguez, Juan Manuel
AB - 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.
JF - Advances in Computational Biology
T3 - Advances in Intelligent Systems and Computing
PB - Springer
VL - 232
UR - http://arxiv.org/abs/1305.5413
ER -