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Cairoli A, Piovani D, Jensen HJ. Forecasting transitions in systems with high-dimensional stochastic complex dynamics: a linear stability analysis of the tangled nature model. Phys Rev Lett. 2014;113(26):264102doi: 10.1103/PhysRevLett.113.264102.
Cairoli, A., Piovani, D., & Jensen, H. J. (2014). Forecasting transitions in systems with high-dimensional stochastic complex dynamics: a linear stability analysis of the tangled nature model. Physical review letters, 113(26), 264102. https://doi.org/10.1103/PhysRevLett.113.264102
Cairoli, Andrea, et al. "Forecasting transitions in systems with high-dimensional stochastic complex dynamics: a linear stability analysis of the tangled nature model." Physical review letters vol. 113,26 (2014): 264102. doi: https://doi.org/10.1103/PhysRevLett.113.264102
Cairoli A, Piovani D, Jensen HJ. Forecasting transitions in systems with high-dimensional stochastic complex dynamics: a linear stability analysis of the tangled nature model. Phys Rev Lett. 2014 Dec 31;113(26):264102. doi: 10.1103/PhysRevLett.113.264102. Epub 2014 Dec 24. PMID: 25615342.
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Phys Rev Lett. 2014 Dec 31;113(26):264102. doi: 10.1103/PhysRevLett.113.264102. Epub 2014 Dec 24.

Forecasting transitions in systems with high-dimensional stochastic complex dynamics: a linear stability analysis of the tangled nature model.

Physical review letters

Andrea Cairoli, Duccio Piovani, Henrik Jeldtoft Jensen

Affiliations

  1. School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, United Kingdom.
  2. Centre for Complexity Science and Department of Mathematics, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.

PMID: 25615342 DOI: 10.1103/PhysRevLett.113.264102

Abstract

We propose a new procedure to monitor and forecast the onset of transitions in high-dimensional complex systems. We describe our procedure by an application to the tangled nature model of evolutionary ecology. The quasistable configurations of the full stochastic dynamics are taken as input for a stability analysis by means of the deterministic mean-field equations. Numerical analysis of the high-dimensional stability matrix allows us to identify unstable directions associated with eigenvalues with a positive real part. The overlap of the instantaneous configuration vector of the full stochastic system with the eigenvectors of the unstable directions of the deterministic mean-field approximation is found to be a good early warning of the transitions occurring intermittently.

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