What if we could predict the economy the way we predict the weather? What if governments could run simulations to forecast the effects of new policies—before they happen? And what if the key to all of this lies in the same chaotic systems that explain spinning roulette wheels and rolling dice?
J. Doyne Farmer is a University of Oxford professor, complexity scientist, and former physicist who once beat Las Vegas casinos using his scientific-based methods. In his recent book “Making Sense of Chaos: A Better Economics for a Better World” Farmer is using those same principles to build a new branch of economics called complexity economics—one that uses big data to help forecast market crashes, design better policies and find ways to confront climate change.
But can we really predict the unpredictable? And how will using chaos theory shake up well-established economic approaches?
