Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/e18030072 | ENTROPY |
Keywords | Field | DocType |
complexity,emergence,self-organization,information,differential entropy,probability distributions | Predictability,Self-organization,Continuous distributions,Probability distribution,Gaussian,Differential entropy,Statistics,Chain rule for Kolmogorov complexity,Mathematics | Journal |
Volume | Issue | ISSN |
18 | 3 | Entropy, 18(3):72. 2016 |
Citations | PageRank | References |
1 | 0.37 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guillermo Santamaría Bonfil | 1 | 6 | 1.50 |
| Nelson Fernandez | 2 | 2 | 1.06 |
| Carlos Gershenson | 3 | 392 | 42.34 |