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Robust Yet Fragile Complexity, or Scale Free Network?

A mention of “Robust-Yet-Fragile” label by resilience author @andrew_zolli led me to John Doyle‘s research at Caltech.  Andrew Zolli writes:

We rightfully add safety systems to things like planes and oil rigs, and hedge the bets of major banks, in an effort to encourage them to run safely yet ever-more efficiently. Each of these safety features, however, also increases the complexity of the whole.  Add enough of them, and soon these otherwise beneficial features become potential sources of risk themselves, as the number of possible interactions — both anticipated and unanticipated — between various components becomes incomprehensibly large.

This, in turn, amplifies uncertainty when things go wrong, making crises harder to correct: Is that flashing alert signaling a genuine emergency? Is it a false alarm? Or is it the result of some complex interaction nobody has ever seen before? [….]

CalTech system scientist John Doyle has coined a term for such systems: he calls them Robust-Yet-Fragile — and one of their hallmark features is that they are good at dealing with anticipated threats, but terrible at dealing with unanticipated ones. As the complexity of these systems grow, both the sources and severity of possible disruptions increases, even as the size required for potential ‘triggering events’ decreases — it can take only a tiny event, at the wrong place or at the wrong time, to spark a calamity.

In an 2007 Discover Magazine article, Carl Zimmer provides a simplified description of research conducted from a theoretical foundation (in Scale Free Networks) in contrast to that from empirical practicalities in control engineering.… Read more (in a new tab)

A mention of “Robust-Yet-Fragile” label by resilience author @andrew_zolli led me to John Doyle‘s research at Caltech.  Andrew Zolli writes:

We rightfully add safety systems to things like planes and oil rigs, and hedge the bets of major banks, in an effort to encourage them to run safely yet ever-more efficiently. Each of these safety features, however, also increases the complexity of the whole.  Add enough of them, and soon these otherwise beneficial features become potential sources of risk themselves, as the number of possible interactions — both anticipated and unanticipated — between various components becomes incomprehensibly large.

This, in turn, amplifies uncertainty when things go wrong, making crises harder to correct: Is that flashing alert signaling a genuine emergency? Is it a false alarm? Or is it the result of some complex interaction nobody has ever seen before? [….]

CalTech system scientist John Doyle has coined a term for such systems: he calls them Robust-Yet-Fragile — and one of their hallmark features is that they are good at dealing with anticipated threats, but terrible at dealing with unanticipated ones. As the complexity of these systems grow, both the sources and severity of possible disruptions increases, even as the size required for potential ‘triggering events’ decreases — it can take only a tiny event, at the wrong place or at the wrong time, to spark a calamity.

In an 2007 Discover Magazine article, Carl Zimmer provides a simplified description of research conducted from a theoretical foundation (in Scale Free Networks) in contrast to that from empirical practicalities in control engineering.… Read more (in a new tab)

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