J. Doyne Farmer (born June 22, 1952) is an American complex systems scientist and entrepreneur with interests in chaos theory, complexity and econophysics. He is Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, Oxford University, where he is also director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School. Additionally, he is an external professor at the Santa Fe Institute. His current research is on complexity economics, focusing on systemic risk in financial markets and technological progress. During his career he has made important contributions to complex systems, chaos, artificial life, theoretical biology, time series forecasting and econophysics. He co-founded Prediction Company, one of the first companies to do fully automated quantitative trading. While a graduate student he led a group that called itself Eudaemonic Enterprises and built the first wearable digital computer, which was used to beat the game of roulette. He is a founder and the Chief Scientist of Macrocosm Inc, a company devoted to scaling up complexity economics methods and reducing them to practice. Farmer's book, Making Sense of Chaos: A Better Economics for a Better World was published by Allen Lane in April 2024.
Biography
Early life Though born in Houston, Texas, Farmer grew up in Silver City, New Mexico. He was strongly influenced by Tom Ingerson, a young physicist and Boy Scout leader who inspired his interest in science and adventure. Scout activities included searching for an abandoned Spanish goldmine to fund a mission to Mars, a road trip to the Northwest Territories and backcountry camping in the Barranca del Cobre. Farmer graduated from Stanford University in 1973 with a BS in physics and went to graduate school at the University of California, Santa Cruz, where he studied physical cosmology under George R. Blumenthal.
Beating roulette While still in graduate school, Farmer and his childhood friend Norman Packard formed a group called Eudaemonic Enterprises. Their goal was to beat the game of roulette and use the proceeds to form a science commune. The word eudaemonia comes from Aristotle and refers to a state of enlightenment derived from a life lived in accordance with reason.
They bought a roulette wheel and did an extensive experimental and theoretical study of its physics. To execute their system, they built the first wearable digital computer, at roughly the same time as the first Apple desktop computer. Farmer hand-coded the three-kilobyte program for the computer in machine language. The program included a floating-point package, a sequencer to perform the calculation, and an operating system that functioned with toe inputs and vibrating outputs. The earliest version of the computer was hidden under the armpits, but a later version was concealed in a shoe. Their scheme took advantage of the fact that typically more than ten seconds elapse from the time the croupier releases the ball until bets are closed. During this time one person measured the position and velocity of the ball and rotor using his big toe to click a switch in his shoe. The computer used this information to predict the likely landing position of the ball. A signal was relayed to a second person, who quickly placed the bets. They made more than eleven trips to Las Vegas, Reno and Tahoe, and achieved a 20% advantage over the house, but suffered persistent hardware problems. This combined with their fear of violence at the hands of the casinos, so that they never played for high stakes and failed to realize the large sums they originally dreamed of.
Chaos and the Dynamical Systems Collective After the roulette project Farmer switched his dissertation topic to chaotic dynamics and joined with James P. Crutchfield, Norman Packard, and Robert Shaw to found the Dynamical Systems Collective (subsequently known by others as the Chaos Cabal). Although they had the blessing of faculty members William L. Burke and Ralph Abraham, they essentially co-advised each other's PhD theses. Their most important contribution was a method for state space reconstruction, that made it possible to visualize and study chaotic attractors based only on a single time series. This has now been used to identify chaotic attractors and study their properties in a wide variety of physical systems. In his PhD thesis in 1981 Farmer showed how varying a parameter of an infinite dimensional system could give rise to a sequence of successively more complicated chaotic attractors, resembling the transition to turbulence. He later developed a method for nonlinear time series forecasting that has been used for exploiting low dimensional chaos to make better short term forecasts. Other work included an improved method for state space reconstruction, and a derivation of the fundamental limits in which this becomes impossible, so that the dynamics become inherently random. He and colleagues also developed a method for determining when chaos can be distinguished from the null hypothesis of a correlated linear random process.
Work
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