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Network catastrophe: self-organized patterns reveal both the instability and the structure of complex networks.

Moon H, Lu TC - Sci Rep (2015)

Bottom Line: Detect the system's transition based on the principal mode of the pattern dynamics; 2.Identify its evolving structure based on the observed patterns.Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

View Article: PubMed Central - PubMed

Affiliation: HRL Laboratories, LLC, 3011 Malibu Canyon Rd, Malibu, CA 90265-4797.

ABSTRACT
Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of-how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description - of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

No MeSH data available.


Related in: MedlinePlus

Self-organization due to instability before a critical transition.When the phase transition breaks the balance between the intrinsic dynamics and the peer influence, the effect of peer influence dominates and a self-organized population behavior emerges. Far from the transition, the dynamics  dominate over the relatively weaker interaction —exhibiting only individual motions. Near the transition, however, the returning force  becomes much weaker than the persisting  —the shifting balance from the dynamics to structure causes the nodes to become highly coordinated, resulting in global patterns.
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f1: Self-organization due to instability before a critical transition.When the phase transition breaks the balance between the intrinsic dynamics and the peer influence, the effect of peer influence dominates and a self-organized population behavior emerges. Far from the transition, the dynamics dominate over the relatively weaker interaction —exhibiting only individual motions. Near the transition, however, the returning force becomes much weaker than the persisting —the shifting balance from the dynamics to structure causes the nodes to become highly coordinated, resulting in global patterns.

Mentions: There are many examples of complex networks showing self-organization. The formation and collapse of speculative market bubble have been largely regarded as the consequence of herd behavior. We postulate that in many cases the herd behavior emerges due to the broken balance between autonomous behavior and peer influence. Figure 1 illustrates the emergence of coordinated behavior. Every entity revolves around its own stable state while exchanging influence with peers or environments, maintaining the balance between these two effects. When a population of such entities goes through a system-wide change that weakens the intrinsic dynamics, the balance is broken and the effect of exchange propagates and dominates, which results in large-scale phenomena. It would then be possible to decode the emergent pattern to identify both the ongoing change and the network of influence.


Network catastrophe: self-organized patterns reveal both the instability and the structure of complex networks.

Moon H, Lu TC - Sci Rep (2015)

Self-organization due to instability before a critical transition.When the phase transition breaks the balance between the intrinsic dynamics and the peer influence, the effect of peer influence dominates and a self-organized population behavior emerges. Far from the transition, the dynamics  dominate over the relatively weaker interaction —exhibiting only individual motions. Near the transition, however, the returning force  becomes much weaker than the persisting  —the shifting balance from the dynamics to structure causes the nodes to become highly coordinated, resulting in global patterns.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4378512&req=5

f1: Self-organization due to instability before a critical transition.When the phase transition breaks the balance between the intrinsic dynamics and the peer influence, the effect of peer influence dominates and a self-organized population behavior emerges. Far from the transition, the dynamics dominate over the relatively weaker interaction —exhibiting only individual motions. Near the transition, however, the returning force becomes much weaker than the persisting —the shifting balance from the dynamics to structure causes the nodes to become highly coordinated, resulting in global patterns.
Mentions: There are many examples of complex networks showing self-organization. The formation and collapse of speculative market bubble have been largely regarded as the consequence of herd behavior. We postulate that in many cases the herd behavior emerges due to the broken balance between autonomous behavior and peer influence. Figure 1 illustrates the emergence of coordinated behavior. Every entity revolves around its own stable state while exchanging influence with peers or environments, maintaining the balance between these two effects. When a population of such entities goes through a system-wide change that weakens the intrinsic dynamics, the balance is broken and the effect of exchange propagates and dominates, which results in large-scale phenomena. It would then be possible to decode the emergent pattern to identify both the ongoing change and the network of influence.

Bottom Line: Detect the system's transition based on the principal mode of the pattern dynamics; 2.Identify its evolving structure based on the observed patterns.Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

View Article: PubMed Central - PubMed

Affiliation: HRL Laboratories, LLC, 3011 Malibu Canyon Rd, Malibu, CA 90265-4797.

ABSTRACT
Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of-how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description - of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

No MeSH data available.


Related in: MedlinePlus