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A black swan event unfortunately has nothing to do with Natalie Portman or Western Australia; he describes an extremely unlikely event, but one which can cause massive upheaval. For example, the global recession of 2008 or, say, pretty much all of 2020.
By definition, no one sees a black swan event coming – but Stanford researchers are trying to change that. They are building a calculus to try to predict when the next graceful neck will lift its head.
“This work is exciting because it is a chance to take the knowledge and computer tools that we build in the laboratory and use them to better understand – even predict or predict – what is happening in the world around us”, Bo Wang, assistant professor of bioengineering at Stanford and lead author of the study, said Stanford News.
Predict unprecedented events
Posted in Computational Biology PLOS, the method is based on natural systems, reports Stanford News, and could be useful in environmental research and health care. (Applications in other areas with black swan events, such as economics and politics, may be more distant.)
According to Stanford News, the work was inspired by Samuel Bray, a research assistant in Wang’s lab.
“The existing methods are based on what we’ve seen to predict what might happen in the future, and that’s why they tend to miss the Black Swan events,” Wang told Stanford News.
“But Sam’s method is different in that it assumes that we only see part of the world. It extrapolates what we’re missing a bit, and it turns out that helps a lot in terms of prediction.”
How it works
Bray had been studying microbial communities for years, and during this time he had observed a few events in which a microbe exploded in population, crushing its rivals. Bray and Wang wondered if this was also happening outside of the lab, and if so, if it could be predicted.
To find out, the two not only needed to find ecological systems with their own black swan events, but also systems with massive and detailed amounts of data, both on the events themselves and on the ecosystem in which they occurred.
Three datasets from natural systems were chosen: measurements of algae, barnacles and mussels on the Kiwi Coast taken monthly for 20 years; Black Sea plankton levels taken twice a week for eight years; and a Harvard study that took net carbon measurements of a forest every half hour since 1991.
They analyzed all of this data using statistical physics – it’s physics that uses probability theory and statistics to try to decipher physical events. Specifically, they used models developed for avalanches and other natural systems with short-term, extreme, and unexpected physical fluctuations – the same qualities that characterize a black swan event.
By taking this analysis, they developed a method to predict a black swan event. The method is supposed to be open to variables such as species and timescale, which allows it to work even with lower quality data.
The method has been tested against the three datasets with which it was constructed. Armed with fragments that showed only the smallest variance, the method accurately predicted the black swan event that would occur.
Wang and Bray hope to broaden their predictor, taking it to other areas where a black swan event can occur, including economics, epidemiology, and physics.
The work joins a growing field of AI algorithms and computational models geared towards extreme events, including those intended for predict forest fires, attend sea search and rescue, and optimize emergency response.