Using complexity economics and chaos theory, J. Doyne Farmer leverages big data to forecast everything from the stock market to roulette.
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?
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 (Yale University Press, 2024), 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.
On this episode of the Big Brains podcast, Farmer explains his work, whether we can really predict the unpredictable, and how using chaos theory could shake up well-established economic approaches:
Read the transcript of this episode. Subscribe to Big Brains on Apple Podcasts and Spotify.
Source: University of Chicago
