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Finding the direction of lowest resilience in multivariate complex systems


Weinans, Els; Lever, J Jelle; Bathiany, Sebastian; Quax, Rick; Bascompte, Jordi; van Nes, Egbert H; Scheffer, Marten; van de Leemput, Ingrid A (2019). Finding the direction of lowest resilience in multivariate complex systems. Journal of the Royal Society Interface, 16(159):20190629.

Abstract

The dynamics of complex systems, such as ecosystems, financial markets and the human brain, emerge from the interactions of numerous components. We often lack the knowledge to build reliable models for the behaviour of such network systems. This makes it difficult to predict potential instabilities. We show that one could use the natural fluctuations in multivariate time series to reveal network regions with particularly slow dynamics. The multidimensional slowness points to the direction of minimal resilience, in the sense that simultaneous perturbations on this set of nodes will take longest to recover. We compare an autocorrelation-based method with a variance-based method for different time-series lengths, data resolution and different noise regimes. We show that the autocorrelation-based method is less robust for short time series or time series with a low resolution but more robust for varying noise levels. This novel approach may help to identify unstable regions of multivariate systems or to distinguish safe from unsafe perturbations.

Abstract

The dynamics of complex systems, such as ecosystems, financial markets and the human brain, emerge from the interactions of numerous components. We often lack the knowledge to build reliable models for the behaviour of such network systems. This makes it difficult to predict potential instabilities. We show that one could use the natural fluctuations in multivariate time series to reveal network regions with particularly slow dynamics. The multidimensional slowness points to the direction of minimal resilience, in the sense that simultaneous perturbations on this set of nodes will take longest to recover. We compare an autocorrelation-based method with a variance-based method for different time-series lengths, data resolution and different noise regimes. We show that the autocorrelation-based method is less robust for short time series or time series with a low resolution but more robust for varying noise levels. This novel approach may help to identify unstable regions of multivariate systems or to distinguish safe from unsafe perturbations.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Scopus Subject Areas:Life Sciences > Biotechnology
Life Sciences > Biophysics
Physical Sciences > Bioengineering
Physical Sciences > Biomaterials
Life Sciences > Biochemistry
Physical Sciences > Biomedical Engineering
Uncontrolled Keywords:stability, resilience, complex networks
Language:English
Date:30 October 2019
Deposited On:04 Feb 2020 13:23
Last Modified:29 Jul 2020 13:19
Publisher:Royal Society Publishing
ISSN:1742-5662
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1098/rsif.2019.0629
PubMed ID:31662072

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