Measuring the Complexity of Continuous Distributions
Author(s)
Santamaría-Bonfil, Guillermo; Fernández, Nelson; Gershenson Garcia, Carlos
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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.
Date issued
2016-02Department
Massachusetts Institute of Technology. Department of Urban Studies and PlanningJournal
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
ISSN
1099-4300