TU Darmstadt / ULB / TUprints

Consistent Quantification of Complex Dynamics via a Novel Statistical Complexity Measure

Keul, Frank ; Hamacher, Kay (2022)
Consistent Quantification of Complex Dynamics via a Novel Statistical Complexity Measure.
In: Entropy, 2022, 24 (4)
doi: 10.26083/tuprints-00021285
Article, Secondary publication, Publisher's Version

[img] Text
entropy-24-00505.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (705kB)
Item Type: Article
Type of entry: Secondary publication
Title: Consistent Quantification of Complex Dynamics via a Novel Statistical Complexity Measure
Language: English
Date: 9 May 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Entropy
Volume of the journal: 24
Issue Number: 4
Collation: 9 Seiten
DOI: 10.26083/tuprints-00021285
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Natural systems often show complex dynamics. The quantification of such complex dynamics is an important step in, e.g., characterization and classification of different systems or to investigate the effect of an external perturbation on the dynamics. Promising routes were followed in the past using concepts based on (Shannon’s) entropy. Here, we propose a new, conceptually sound measure that can be pragmatically computed, in contrast to pure theoretical concepts based on, e.g., Kolmogorov complexity. We illustrate the applicability using a toy example with a control parameter and go on to the molecular evolution of the HIV1 protease for which drug treatment can be regarded as an external perturbation that changes the complexity of its molecular evolutionary dynamics. In fact, our method identifies exactly those residues which are known to bind the drug molecules by their noticeable signal. We furthermore apply our method in a completely different domain, namely foreign exchange rates, and find convincing results as well.

Uncontrolled Keywords: complexity, co-evolution, Jensen–Shannon, entropy
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-212856
Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 570 Life sciences, biology
Divisions: 10 Department of Biology > Computational Biology and Simulation
Date Deposited: 09 May 2022 13:45
Last Modified: 14 Nov 2023 19:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21285
PPN: 499758366
Export:
Actions (login required)
View Item View Item