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Predicting critical transitions in complex systems is vital for sustainability. This study introduces a novel dynamical network marker (DNM)-based indicator, outperforming conventional methods in forecasting abrupt system shifts.

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Area of Science:

  • Complex systems analysis
  • Dynamical systems theory
  • Network science

Background:

  • Predicting critical transitions is crucial for sustainable societies.
  • Abrupt shifts in system states pose significant challenges.
  • Existing indicators may lack robustness across diverse network structures.

Purpose of the Study:

  • To propose a new indicator for predicting critical transitions in multivariate dynamical systems.
  • To leverage the dynamical network marker (DNM) concept for transition prediction.
  • To evaluate the proposed indicator's efficacy in complex network systems.

Main Methods:

  • Developed a DNM-based indicator using the sample covariance matrix of state variables.
  • Defined the dynamical network marker (DNM) via Jacobian matrix eigendecomposition.
  • Tested the indicator in a complex network system model with a harvesting component.

Main Results:

  • The proposed DNM-based indicator effectively predicted critical transitions.
  • The indicator demonstrated superiority over a conventional method.
  • Performance remained consistent across various network structure characteristics.

Conclusions:

  • The novel DNM-based indicator is a reliable precursor for critical transitions.
  • This method offers improved prediction capabilities for complex dynamical systems.
  • The findings contribute to enhanced system stability management in sustainable contexts.