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Self-organization of dragon king failures
Yuansheng Lin1,2,3, Keith Burghardt4, Martin Rohden3
1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
This study models cascading failures using network systems, distinguishing contained "black swan" events from massive "dragon king" failures. A critical initial failure size predicts dragon kings, which can be mitigated by a new control strategy.
Area of Science:
- Network science
- Complex systems
- Statistical physics
Background:
- Cascading failures are often studied using self-organized criticality models.
- Real-world networks exhibit a trade-off between node degradation and reinforcement.
Purpose of the Study:
- To introduce a network model that captures node self-organization for failure protection.
- To differentiate between contained failures and large-scale cascading events.
- To identify predictors and control strategies for massive system failures.
Main Methods:
- Developed a network model with weakly and strongly protected nodes.
- Simulated failure propagation under varying conditions.
- Classified failure events as "black swans" or "dragon kings" based on their scale and mechanism.
Main Results:
- If strong nodes are invulnerable, failures are confined, producing power-law distributions.
- If strong nodes can fail, cascading failures ("dragon kings") can occur, affecting over 99.9% of nodes.
- Dragon kings are self-organized and triggered by an initial weak node cluster exceeding a critical size.
Conclusions:
- The size of the initial weak cluster failure accurately predicts dragon king events.
- A control strategy can significantly reduce the occurrence of dragon kings and other large-scale failures.
- This model provides insights into managing catastrophic failures in complex systems.
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