From the perspective of complex systems, a range of events – from chemistry and biology to extreme weather and population ecology – can be viewed as large-scale self-emergent phenomena that occur as a consequence of deteriorating stability. Based on observing the self-organized patterns associated with these phenomena, the elusive goal has been the ability to interpret these emergent patterns to predict the related critical events. Recently, scientists at HRL Laboratories, LLC in Malibu, California sought to determine if there was a quantifiable relationship between these patterns and the network of interactions characterizing the event. By limiting their working definition of self-organization to spontaneous order emergence resulting from a non-equilibrium phase transition (that is, a change in a feature of a physical system – one that is not simply isolated from the rest of the universe –that results in a discrete transition of that system to another state), the researchers were able to detect the transition based on the principal mode of the pattern dynamics, and identify its evolving structure based on the observed patterns. They found 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. The scientists then validated their analysis on real-world markets and showed common self-organized behavior near critical transitions, such as housing market collapse and stock market crashes, thereby providing a proof-of-concept that their goal of being able to detect critical events before they are in full effect is possible.
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