Measuring the Complexity of Continuous Distributions
Name
Santamaria-Bonfil-2016-Measuring the Comple.pdf
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700.97 KB
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Author(s)
Santamaría-Bonfil, Guillermo
Fernández, Nelson
Gershenson Garcia, Carlos
Date Issued
February 2016
Journal
Entropy
Publisher
MDPI AG
Citation
Santamaría-Bonfil, Guillermo, Nelson Fernández, and Carlos Gershenson. “Measuring the Complexity of Continuous Distributions.” Entropy 18, no. 3 (February 26, 2016): 72.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/e18030072
