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When Networks Network

When Networks Network | FuturICT Journal Publications | Scoop.it

When Networks Network

By Elizabeth Quill

 

- When networks depend on other networks, such as a communications network that relies on a power grid, failure can cascade back and forth between the two. This behavior may explain sudden breakdowns in interacting systems. Thus, the effects of an attack on a single node can reduce an übernetwork  that starts with 12 operating nodes to just four.- 

 

Once studied solo, systems display surprising behavior when they interact.

 

Half a dozen times each night, your slumbering body performs a remarkable feat of coordination.

 

During the deepest throes of sleep, the body’s support systems run on their own timetables. Nerve cells hum along in your brain, their chitchat generating slow waves that signal sleep’s nether stages. Yet, like buses and trains with overlapping routes but unsynchronized schedules, this neural conversation has little to say to your heart, which pumps blood to its own rhythm through the body’s arteries and veins. Air likewise skips into the nostrils and down the windpipe in seemingly random spits and spats. And muscle fluctuations that make the legs twitch come and go as if in a vacuum. Networks of muscles, of brain cells, of airways and lungs, of heart and vessels operate largely independently.

 

Every couple of hours, though, in as little as 30 seconds, the barriers break down. Suddenly, there’s synchrony. All the disjointed activity of deep sleep starts to connect with its surroundings. Each network — run via the group effort of its own muscular, cellular and molecular players — joins the larger team.

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Quantifying the Behavior of Stock Correlations Under Market Stress: Tobias Preis, Helen Susannah Moat, H. Eugene Stanley & Steven R. Bishop

Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by

normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.

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