Suggestions
Doyne Farmer
Director, Complexity Economics, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford
J. Doyne Farmer is a prominent complex systems scientist and economist currently serving as the Director of Complexity Economics at the Institute for New Economic Thinking at the Oxford Martin School.15 He is also the Baillie Gifford Professor of Complex Systems Science at the University of Oxford's Smith School of Enterprise and the Environment.13
Farmer's career spans over four decades, during which he has made significant contributions to various fields:
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Current research: His current work focuses on economics, including agent-based modeling, financial instability, and technological progress.12
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Academic positions: In addition to his roles at Oxford, Farmer is an External Professor at the Santa Fe Institute.13
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Industry experience: He was a founder of Prediction Company, a quantitative automated trading firm sold to UBS in 2006.15
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Early career: During the 1980s, Farmer worked at Los Alamos National Laboratory, where he founded the Complex Systems Group.14
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Pioneering work: As a graduate student in the 1970s, he built the first wearable digital computer, which was used to predict roulette outcomes.15
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Recent ventures: Farmer is currently the Chief Scientist at Macrocosm, a company he founded to apply complexity economics to climate change and green energy transition challenges.13
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Education: He holds a Ph.D. in Physics from the University of California, Santa Cruz, and a Bachelor of Science in Physics from Stanford University.3
Farmer has received several awards, including the Hertz Fellowship (1978), J. Robert Oppenheimer Fellowship (1983), and the Alexander von Humboldt Award (2011).23
Highlights
We have a new paper out in Cell Press's Joule - Lennart Baumgärtner, Rupert Way, Matthew Ives and I explain how unsound technology forecasting practice, including overfitting, data selection bias, and ad hoc assumptions, lead to unreliable conclusions when modeling technology. (Already challenging enough due to data limitations, deep uncertainty, and the complex social and technological dynamics involved in the evolution of energy systems.)
This is vitally important for informing energy policy (and the green energy transition). We flag several cases where this has been problematic - read the article for our general takeaways and suggestions for how statistical testing should be conducted to avoid such problems in the future.
Halfway through recording the audiobook of Making Sense of Chaos at Electric Breeze Audio Productions

