Finding the direction of lowest resilience in multivariate complex systems

Els Weinans*, Jelle Lever, Sebastian Bathiany, Rick Quax, Jordi Bascompte, Egbert H. Van Nes, Marten Scheffer, Ingrid A. Van De Leemput

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Article number20190629
JournalJournal of the Royal Society Interface
Volume16
Issue number159
DOIs
Publication statusPublished - 30 Oct 2019

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Large scale systems
Time series
Autocorrelation
Noise
Ecosystems
Ecosystem
Brain
Direction compound

Keywords

  • Complex networks
  • Resilience
  • Stability

Cite this

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title = "Finding the direction of lowest resilience in multivariate complex systems",
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.",
keywords = "Complex networks, Resilience, Stability",
author = "Els Weinans and Jelle Lever and Sebastian Bathiany and Rick Quax and Jordi Bascompte and {Van Nes}, {Egbert H.} and Marten Scheffer and {Van De Leemput}, {Ingrid A.}",
year = "2019",
month = "10",
day = "30",
doi = "10.1098/rsif.2019.0629",
language = "English",
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journal = "Journal of the Royal Society, Interface",
issn = "1742-5689",
publisher = "Royal Society of London",
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Finding the direction of lowest resilience in multivariate complex systems. / Weinans, Els; Lever, Jelle; Bathiany, Sebastian; Quax, Rick; Bascompte, Jordi; Van Nes, Egbert H.; Scheffer, Marten; Van De Leemput, Ingrid A.

In: Journal of the Royal Society Interface, Vol. 16, No. 159, 20190629, 30.10.2019.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Weinans, Els

AU - Lever, Jelle

AU - Bathiany, Sebastian

AU - Quax, Rick

AU - Bascompte, Jordi

AU - Van Nes, Egbert H.

AU - Scheffer, Marten

AU - Van De Leemput, Ingrid A.

PY - 2019/10/30

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N2 - 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.

AB - 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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KW - Stability

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