Early warnings for multi-stage transitions in dynamics on networks
Neil G MacLaren1, Prosenjit Kundu1, Naoki Masuda1,2
1Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA.
Journal of the Royal Society, Interface
|March 15, 2023
Summary
Anticipating major changes in complex systems is crucial. This study reveals that specific network nodes can effectively predict multi-stage transitions, even outperforming analyses of the entire system.
Area of Science:
- Complex systems science
- Network theory
- Dynamical systems
Background:
- Complex systems often exhibit multi-stage transitions, where parts shift at different times.
- Predicting abrupt changes in these heterogeneous networks is practically important.
Purpose of the Study:
- To investigate early warning signals for networked systems undergoing multi-stage transitions.
- To determine if subsets of nodes can effectively predict these transitions.
Main Methods:
- Analyzing networked systems with multi-stage transitions.
- Calculating early warning signals using network structure and transition dynamics.
- Evaluating the performance of node subsets versus the entire network.
Main Results:
- Effective early warning signals can be calculated by considering both the transition stage and network structure.
- Small subsets of nodes can provide early warnings as effectively as, or better than, using all nodes.
- Optimal node selection for early warning signals depends on the tipping direction and current transition stage.
Conclusions:
- Knowledge of network structure and transition dynamics enhances early warning signals.
- Identifying strategic 'sentinel' nodes can improve prediction of complex system shifts.
- The choice of sentinel nodes is context-dependent, varying with transition dynamics.
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