Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally

Manuel de J. Luevano; Alejandro Puga

doi:10.20944/preprints202403.1569.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 25 March 2024 / Approved: 26 March 2024 / Online: 26 March 2024 (10:07:13 CET)

In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. To do this, its randomness is estimated using three metrics, of which algorithmic complexity is the only one capable of revealing both transitions towards the chaotic regime and the algorithmic contribution of certain states to the transition diagram and their relationship with the order they occupy in the temporal evolution of the respective RBN.

Random boolean networks; entropy; algorithmic complexity; compressibility

Computer Science and Mathematics, Computer Science

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Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally

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