In 2006, economists at the Federal Reserve Bank of New York became concerned about the overheating US housing market. They worried that the bubble might burst and used their best model to predict the potential impact of a 20 per cent drop in house prices. Surprisingly, the model predicted that not much would happen. However, shortly after, house prices plummeted by almost exactly 20 per cent, triggering one of the worst global economic downturns in a century.
Economics often faces criticism for being a pseudoscience, with complex mathematical formulas that mask its subjectivity and a track record of inaccurate predictions. J. Doyne Farmer believes there is a better way. In his new book, “Making Sense of Chaos,” he explores why standard economic approaches frequently fail and proposes a radical alternative known as complexity economics. This approach views economies as systems similar to natural ecosystems or Earth’s climate, and uses large-scale computer simulations to model how billions of people interact within the global economy.
Currently holding positions at the University of Oxford and the Santa Fe Institute in New Mexico, Farmer’s path to economics has been unconventional. He began by dropping out of graduate school to build the world’s first wearable computer, which he used to beat the casino at roulette. In the 1990s, he founded Prediction Company, applying similar principles to the stock market. As a pioneer of chaos theory and complex systems, Farmer believes that complexity economics has now reached a point where it can provide reliable predictions about various economic phenomena.