In case you might have missed it, I think this one is important...
Predicting tipping points before they occur
By Elizabeth Weise, USA TODAY
Say you're in a canoe on a lake, dry and firmly seated. A fish swims by and you lean over to take a look. You lean a little further, and a little further still, until, with a surge of panic you realize you're starting to fall over. You being to flail your arms, causing the canoe and you to rock wildly back and forth, but it's no good: Over the side you go.
Now you're in a new stable state ? treading water next to an upside down canoe. And getting back to your former state ? un-waterlogged in an upright canoe ? will take a lot more work than falling out did.
You've just experienced a tipping point.
However, if you've been following work over the past 30 years in systems theory, you'll recognize this scenario as what scientists call a bifurcation point ? the moment when a stable system flips over into a new stable state, after a period of rapid change.
Now some of the most prominent scientists in that field have published a new paper on detecting early warning signals before a system changes. Titled "Early-warning signals for critical transitions," the review paper is in this week's issue of the journal Nature.
Though exceedingly complex, the gist of their work is simple: detecting patterns that tend to emerge in systems just before they hit a tipping point, hopefully in time to stop the process.
"This is a very important paper," says Brian Walker, a fellow at the Stockholm Resilience Center at the University of Stockholm in Sweden.
"The big question they're trying to answer is, how the hell do you know when it's coming? Is there any way you can get an inkling of a looming threshold, something that might be a warning signal that you're getting to one of the crucial transition points?"
"The fascinating thing is that we found that very different systems react the same way and appear to obey the same universal laws as they are getting close to a tipping point," says Marten Scheffer, lead author on the paper and an ecologist at Wageningen University in the Netherlands.
Work on these theories began in the 1970s. At a meeting of researchers looking at these signals convened by Scheffer in Holland in 2007, scientists began to see examples in all sorts of places.
"We began to realize that there was really pretty cool and fundamental thing going on here," says Stephen Carpenter, one of the paper's authors and a lake ecologist at the University of Wisconsin-Madison.
Discerning these tipping points before they happen would be huge, says Carpenter.
"Managing the environment is like driving a car in the dark in the fog on the edge of a cliff. You know the edge is out there, but it's dark and foggy," he says. "We're really great at knowing where thresholds are after we fall off the cliff, but that's not very helpful."
What's fascinating about this concept of bifurcation points is that like fractals (shapes and patterns that reoccur at different sizes throughout systems and in nature), once you know about it, you realize that it's everywhere, you just hadn't realized it before.
Examples of systems that these critical transitions have been found in include:
?algae blooms in lakes ?asthma attacks ?climate change ?desertification ?epileptic seizures ?fisheries collapse ?migraines ?financial systems
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Not yet predictive
While their work can't predict all transition points, they've begun to tease out universal principles, says Scheffer.
What the researches have shown is that these early warning signals seem to occur differently in different kinds of systems, but there's some regularity to them within a set of systems.
Systems can also being to "flicker," rapidly oscillating between to states before finally settling into a stable one.
There's also an interesting finding that as a system gets close to tipping, it becomes more like systems around it. This is seen often in financial markets and in social settings where the attitudes of individuals towards certain issues are affected by what their peers think.
Another warning signal is variability ? a lot of change back and forth.
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