ScienceNews | Brains have seizures, ecosystems collapse, economies crash — and it sure would be great if we could predict when. Despite the complexity of these seemingly disparate events, recent research suggests that tipping points are foreseeable.
A study published online February 2 in PLoS Computational Biology offers a way to discern when a complex system such as a fishery may be teetering towards collapse. The new work uses mathematical indicators to help researchers understand systems when there’s not enough data to build the kind of complex supercomputer simulations that are typically used to study things like climate change. And other recent studies have turned up even more mathematical red flags that a system is approaching a point of no return.
“At one end, there’s the brute force approach,” says study coauthor Steven Lade of the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany. “You make a very detailed model of the system, try and add everything that’s going on, and run it into the future.”
But scientists often don’t know enough of the details to make such simulations accurate. “The more specific you can be, the better, but you shouldn’t be specific about the things you don’t know about,” says Marten Scheffer of Wageningen University in the Netherlands.
The latest work makes use of generic signals that a system might be going awry but also allows for any specific information that may be available. Lade and his Max Planck colleague Thilo Gross start with that known information, such as the year-end catch numbers for a particular fish and what that species eats. Then they link that information to general math that describes a system at near equilibrium. By adding to their simulation an outside perturbation that might push the system over the edge, such as habitat destruction or a new disease, the researchers can track the stability of the system through time. In the fisheries case the researchers used to test their model, the method revealed a glaring signal when collapse was imminent that became easier to distinguish as the collapse approached.
“It’s making the point that it’s better to use the information that you have,” Scheffer says. “Anything you know about the system can sharpen your search image.”
Surprisingly, when even less data is available than the scant amount used in the new work, there are some simple mathematical clues that can provide a red alert that an abrupt shift is coming, be it in a tangle of brain cells or an entire ecosystem.
“In complex systems it’s really hard to predict anything,” says Scheffer. “But the fascinating thing is there are universal mathematical principles that should hold.” Much of this theory has been around for decades, Scheffer says, but no one knew whether the math would work for predicting major transitions in real systems. Now experimental data is starting to come in —mostly from the realm of ecology — and it suggests that predicting the future may soon be more about science than soothsaying.
In the Jan. 29 Nature, for example, a team led by Scheffer reported success using one mathematical test of an approaching tipping point. Theory says that when a shift is coming, a system exhibits what scientists call a critical slowing down. Normally, a really stable system quickly recovers after being perturbed. But when everything is about to come unglued, the recovery time from even a small perturbation becomes slower and slower.
Whether you’re looking at a timescale of years or centuries for a climate system, or at the scale of milliseconds in the brainwaves of someone about to have an epileptic seizure, this slowing down of recovery time appears to be an important signal that some serious action is about to go down.
A study published online February 2 in PLoS Computational Biology offers a way to discern when a complex system such as a fishery may be teetering towards collapse. The new work uses mathematical indicators to help researchers understand systems when there’s not enough data to build the kind of complex supercomputer simulations that are typically used to study things like climate change. And other recent studies have turned up even more mathematical red flags that a system is approaching a point of no return.
“At one end, there’s the brute force approach,” says study coauthor Steven Lade of the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany. “You make a very detailed model of the system, try and add everything that’s going on, and run it into the future.”
But scientists often don’t know enough of the details to make such simulations accurate. “The more specific you can be, the better, but you shouldn’t be specific about the things you don’t know about,” says Marten Scheffer of Wageningen University in the Netherlands.
The latest work makes use of generic signals that a system might be going awry but also allows for any specific information that may be available. Lade and his Max Planck colleague Thilo Gross start with that known information, such as the year-end catch numbers for a particular fish and what that species eats. Then they link that information to general math that describes a system at near equilibrium. By adding to their simulation an outside perturbation that might push the system over the edge, such as habitat destruction or a new disease, the researchers can track the stability of the system through time. In the fisheries case the researchers used to test their model, the method revealed a glaring signal when collapse was imminent that became easier to distinguish as the collapse approached.
“It’s making the point that it’s better to use the information that you have,” Scheffer says. “Anything you know about the system can sharpen your search image.”
Surprisingly, when even less data is available than the scant amount used in the new work, there are some simple mathematical clues that can provide a red alert that an abrupt shift is coming, be it in a tangle of brain cells or an entire ecosystem.
“In complex systems it’s really hard to predict anything,” says Scheffer. “But the fascinating thing is there are universal mathematical principles that should hold.” Much of this theory has been around for decades, Scheffer says, but no one knew whether the math would work for predicting major transitions in real systems. Now experimental data is starting to come in —mostly from the realm of ecology — and it suggests that predicting the future may soon be more about science than soothsaying.
In the Jan. 29 Nature, for example, a team led by Scheffer reported success using one mathematical test of an approaching tipping point. Theory says that when a shift is coming, a system exhibits what scientists call a critical slowing down. Normally, a really stable system quickly recovers after being perturbed. But when everything is about to come unglued, the recovery time from even a small perturbation becomes slower and slower.
Whether you’re looking at a timescale of years or centuries for a climate system, or at the scale of milliseconds in the brainwaves of someone about to have an epileptic seizure, this slowing down of recovery time appears to be an important signal that some serious action is about to go down.
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