In 2006, economists at the Federal Reserve Bank of New York became concerned about the overheating US housing market. They feared that the bubble might burst, so they used their best model to predict the outcome if house prices were to drop by 20 per cent. Surprisingly, the model predicted that not much would happen. However, shortly after this prediction, house prices did indeed fall by almost exactly 20 per cent, resulting in one of the worst global economic downturns in history.
Economics is often criticized for its dense mathematical formulas that do not always accurately predict outcomes. J. Doyne Farmer believes there is a better way. In his new book, Making Sense of Chaos, Farmer explores why traditional economic approaches fail and introduces a revolutionary concept called complexity economics. This approach treats economies as complex systems similar to natural ecosystems or Earth’s climate. By using giant computer simulations based on these ideas, a more accurate representation of how billions of people interact within the global economy can be achieved.
Currently holding positions at the University of Oxford and the Santa Fe Institute in New Mexico, Farmer’s path into economics has been unconventional. Starting by dropping out of graduate school, he built the world’s first wearable computer and used it to beat the casino at roulette. In the 1990s, he founded Prediction Company where he applied similar principles to the stock market. As a pioneer of chaos theory and complex systems, Farmer believes that complexity economics has finally emerged as a reliable method for making predictions about the economy.