It appears that the standard tools used to identify chaotic signatures might be missing lots of hidden chaos — especially in systems that seem like they’re not chaotic at all.

Chaos theory is famously associated with so-called “strange attractors,” marked by a telltale butterfly-wing shape (see above). But according to a new paper by two University of Maryland mathematicians, sometimes chaos looks more like “a strange repeller,” or something else entirely.

“Scientists talk a lot about chaos and they don’t agree exactly on the [mathematical] definition,” Brian Hunt, one of the co-authors, told Gizmodo. So when the journal *Chaos* asked him and his UMD collaborator, Edward Ott, to write something for its 25th anniversary, he jumped at the chance to to come with a broader definition of chaos — a kind of universal signature, if you will — that would provide a handy mathematical tool for ferreting out where chaos might be hiding in previously unsuspected areas.

When scientists talk about something being “chaotic,” they don’t mean it in the colloquial sense of being completely random. They mean that the system is so incredibly sensitive to early initial conditions that, as it evolves over time, it does so in unpredictable ways, obscuring the underlying rules of order. The classic example is the “butterfly effect”: a buttery flaps its wings in Brazil and causes a tornado in Texas. This is in sharp contrast to a simple linear system - say, the swinging pendulum on a grandfather clock — where if you know a few key variables at any given point in time, it’s a fairly trivial matter to predict how the system will behave. Tell me what’s happening now, and I can use the laws of physics to tell you what will happen next, or what happened before. Those kinds of system are deemed “deterministic.”

Alas, most real world systems aren’t that simple — even those that technically qualify as deterministic, like predicting the trajectories of billiard balls after the break. Still, if you could gather enough information, in principle you’d be able to make predictions, especially with the help of a powerful computer.

But a chaotic system — the weather is the most common example — is inherently unpredictable because there are so many interlinked variables, that even a tiny change in one of them gets amplified over time through a kind of feedback loop until the system goes critical. Here, let bad boy mathematician Ian Malcolm (he who suffers from “a deplorable excess of personality”) explain:

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