Measuring the Complexity of Continuous Distributions

TitleMeasuring the Complexity of Continuous Distributions
Publication TypeJournal Article
Year of Publication2016
AuthorsSantamaría-Bonfil, G, Fernández, N, Gershenson, C
JournalEntropy
Volume18
Pagination72
ISSN1099-4300
Abstract

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.

URLhttp://www.mdpi.com/1099-4300/18/3/72
DOI10.3390/e18030072