Complexity Control

TL;DR

This paper presents a dynamic complexity control model that aligns system complexities based on temporal measures, with potential applications in brain function analysis and system regulation.

Contribution

The paper introduces a novel dynamic model for complexity control using inverse power law indices, linking it to empirical brain complexity measures and practical applications.

Findings

Model effectively describes complexity synchronization phenomena

Potential for restoring or disrupting system complexity

Applicable to brain rehabilitation and malicious system control

Abstract

We introduce a dynamic model for complexity control (CC) between systems, represented by time series characterized by different temporal complexity measures, as indicated by their respective inverse power law (IPL) indices. Given the apparent straightforward character of the model and the generality of the result, we formulate a hypothesis based on the closeness of the scaling measures of the model to the empirical complexity measures of the human brain. CC is a proper model for describing the recent experimental results, such as the rehabilitation in walking arm in arm and the complexity synchronization effect. The CC effect can lead to the design of mutual-adaptive signals to restore the misaligned complexity of maladjusted organ networks or, on the other hand, to disrupt the complexity of a malicious system and lower its intelligent behavior.