How to See Disaster Coming

Researchers analyze mathematics of rapid change in complex systems and find room for improvement—and hints at mitigation.
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Destruction caused by a 2011 earthquake in Christchurch, New Zealand. (Photo: Darrenp/Shutterstock)

Destruction caused by a 2011 earthquake in Christchurch, New Zealand. (Photo: Darrenp/Shutterstock)

Catastrophic events aren't particularly unusual in our world, but with everything from the 2008 housing market crash to global climate change in the headlines, scientists are increasingly interested in how we see tipping points and resulting catastrophes coming. But in searching for signs, a new study suggests researchers may have overlooked some key features of the real world—and hints at some ways to avoid disaster.

Forecasting catastrophe is hot stuff these days, but it remains a tricky business. In order to make accurate predictions about some of the world's most complex systems, including earthquakes, financial markets, and even our brains, researchers need accurate models. Yet many current models make the unrealistic assumption that the world is deterministic; that is, that the future is perfectly predicable based on its present state.

In reality, the world is usually a little bit random—what scientists call stochastic. Whether the world is deterministic or stochastic can have a profound impact on disaster prediction. In deterministic models of plant growth, for example, desert exists when there's little rainfall, and vegetation dominates when there's a lot. In between, both desert and extensive plant life are possible, and which prevails depends on the precise details of past rainfall and plant growth. In such circumstances, even small nudges to the ecosystem could rapidly—perhaps permanently—turn verdant land into desert.

Computer simulations and mathematical analyses revealed that while randomness doesn't eliminate the possibility of rapid, catastrophic transitions, it can certainly reduce it.

Toss in a little randomness in rainfall or the distribution of plants, however, and it might smooth the transition from verdant to barren. It could even make that transition reversible, though exactly what would smooth, prevent, or reverse a catastrophic transition remains unclear.

Now, University of Granada physics graduate student Paula Villa Martín, working with professors Juan Bonachela, Simon Levin, and Miguel Muñoz, has begun to address those issues by using a simple mathematical model that captures the essential features of the plant growth model and many others—namely, the existence of rapid transitions triggered by small changes to the system—while also incorporating some randomness.

Computer simulations and mathematical analyses revealed that while randomness doesn't eliminate the possibility of rapid, catastrophic transitions, it can certainly reduce it. In particular, increased randomness and reduced diffusion—in the plant growth example, those correspond to greater variation in the density of plants and to seeds or other resources that don't spread as far from their source—both smooth what would otherwise be dramatic shifts from one regime to another.

Beyond their scientific value, those observations also suggest ways to prevent catastrophe. For example, randomizing the locations where cattle graze, rather than allowing them to graze uniformly over the landscape, could make that land more robust to ecological disaster, the authors argue.

"Given the growing concerns about the effect of anthropogenic pressures on climate and biodiversity, we hope that this framework will help to understand better and open new research roads to explore possible strategies to mitigate the radical and harmful effects of sudden undesirable regime shifts," the team writes today in Proceedings of the National Academy of Sciences.

Quick Studies is an award-winning series that sheds light on new research and discoveries that change the way we look at the world.

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