Dynamical noise can enhance high-order statistical structure in complex systems
File(s)
Accepted version
Author(s)
Orio, Patricio
Mediano, Pedro AM
Rosas, Fernando E
Type
Journal Article
Abstract
Recent research has provided a wealth of evidence highlighting the pivotal role of high-order interdependencies in supporting the information-processing capabilities of distributed complex systems. These findings may suggest that high-order interdependencies constitute a powerful resource that is, however, challenging to harness and can be readily disrupted. In this paper, we contest this perspective by demonstrating that high-order interdependencies can not only exhibit robustness to stochastic perturbations, but can in fact be enhanced by them. Using elementary cellular automata as a general testbed, our results unveil the capacity of dynamical noise to enhance the statistical regularities between agents and, intriguingly, even alter the prevailing character of their interdependencies. Furthermore, our results show that these effects are related to the high-order structure of the local rules, which affect the system’s susceptibility to noise and characteristic time scales. These results deepen our understanding of how high-order interdependencies may spontaneously emerge within distributed systems interacting with stochastic environments, thus providing an initial step toward elucidating their origin and function in complex systems like the human brain.
Date Issued
2023-12
Date Acceptance
2023-10-31
Citation
Chaos: an interdisciplinary journal of nonlinear science, 2023, 33 (12)
ISSN
1054-1500
Publisher
American Institute of Physics
Journal / Book Title
Chaos: an interdisciplinary journal of nonlinear science
Volume
33
Issue
12
Copyright Statement
Copyright © 2023 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in [citation of article] and may be found at https://doi.org/10.1063/5.0163881
Identifier
https://pubs.aip.org/aip/cha/article/33/12/123103/2925781/Dynamical-noise-can-enhance-high-order-statistical
Subjects
Mathematics
Mathematics, Applied
Physical Sciences
Physics
Physics, Mathematical
Science & Technology
Publication Status
Published
Article Number
123103
Date Publish Online
2023-12-04