Ben Brubaker writes:

Predicting complex systems like the weather is famously difficult. But at least the weather’s governing equations don’t change from one day to the next. In contrast, certain complex systems can undergo “tipping point” transitions, suddenly changing their behavior dramatically and perhaps irreversibly, with little warning and potentially catastrophic consequences.

On long enough timescales, most real-world systems are like this. Consider the Gulf Stream in the North Atlantic, which transports warm equatorial water northward as part of an oceanic conveyor belt that helps regulate Earth’s climate. The equations that describe these circulating currents are slowly changing due to the influx of fresh water from melting ice sheets. So far the circulation has slowed gradually, but decades from now it may abruptly grind to a halt.

“Suppose everything is OK now,” said Ying-Cheng Lai, a physicist at Arizona State University. “How do you tell that it’s not going to be OK in the future?”

In a series of recent papers, researchers have shown that machine learning algorithms can predict tipping-point transitions in archetypal examples of such “nonstationary” systems, as well as features of their behavior after they’ve tipped. The surprisingly powerful new techniques could one day find applications in climate science, ecology, epidemiology and many other fields. [Continue reading…]