Doyne Farmer

Doyne Farmer, Director of the Complexity Economics program at Oxford, dives into the chaotic nature of economies and the inadequacies of traditional economic models. He highlights how chaos theory can reshape our understanding of financial systems and promote more accurate predictions. Farmer critiques the reliance on equilibrium, advocating for models that embrace dynamic interactions and real-world complexities. He also draws fascinating parallels between economic systems and ecological dynamics, exploring how innovation intertwines with complexity in markets.

A newly published research paper out of Oxford suggests that a rapid energy transition will not "cost" anything -- it will save nearly a trillion dollars relative to the no-transition case. And the faster we move, the more money we save. I talk with complex-systems scientist and co-author Doyne Farmer about his optimistic projections. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.volts.wtf/subscribe

Dr. Doyne Farmer, Baillie Gifford Professor of Complex Systems Science at Oxford University, dives into complexity theory and its impact on energy systems. He explains how simple interactions can lead to significant outcomes in our energy transition. The conversation highlights technological advancements in solar and battery innovations, how hydrocarbons can transform the oil and gas industry, and the potential of renewable systems for rural communities. Farmer also discusses consumer behavior in embracing electric vehicles and the need for innovative economic solutions in the energy sector.

In 2021, a group of Scholars at Oxford University published a paper that made big waves in the energy world. It argued that key clean energy technologies — wind, solar, batteries, and electrolyzers — are on learning curves which guarantee that, if they are deployed at the scale required to reach zero carbon, they will get extremely cheap.This is, as they say, big if true. In September, I had one of the lead authors, Doyne Farmer, on Volts to discuss the paper in-depth. He made a convincing case for the paper’s thesis, but when I asked him why these technologies were on learning curves and others weren't, he could only speculate.That's the question that's been on my mind ever since. Why are some clean-energy technologies getting rapidly cheaper while others aren't? What is it about particular technologies that make them amenable to learning curves?I cast that question to the academic gods, and lo, they returned with a paper, and that paper is what we’re here to discuss today. It’s called “Accelerating Low-Carbon Innovation,” by Abhishek Malhotra of the School of Public Policy at the Indian Institute of Technology in New Delhi, India, and Tobias Schmidt of the Swiss Federal Institute of Technology in Zurich, Switzerland.It sets out to chart technologies against two basic axes: design complexity and need for customization. That creates a schema that can help illuminate why some technologies developed quicker than others.I don't want to say much more than that, since I have my Malhotra and Schmidt here with me to help explain. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.volts.wtf/subscribe

Doyne Farmer, an innovative scientist and entrepreneur known for his work in chaos theory and complex systems, shares fascinating insights. He recounts his daring win at a Las Vegas casino using one of the first wearable computers, demonstrating the predictability within apparent randomness. Farmer discusses applying complex systems science to economics, stressing the importance of predictive models in addressing climate change and economic crises. His journey reveals how the intricacies of chaos can lead to transformative solutions in our unpredictable world.

Doyne Farmer, a prominent figure in complexity economics, discusses his journey from chaos theory to economics. He emphasizes the importance of multidisciplinary work and applying complexity science to climate change. The conversation delves into the development of agent-based models for the economy and the challenges in predicting economic impacts, such as during the COVID pandemic.

J. Doyne Farmer, a complexity scientist and professor at Oxford, once outsmarted casinos with his scientific insights. He dives into the intriguing idea of predicting economies like weather patterns, using chaos theory and big data. Farmer discusses the potential of agent-based modeling to revolutionize economic forecasting and addresses the challenges of understanding complex systems. He also highlights how complexity economics could reshape public policy, tackle climate issues, and pave the way for sustainable growth in our unpredictable world.

Doyne Farmer, a renowned physicist and mathematician at the University of Oxford, joins to explore how complexity theory can reshape economics. They discuss the shortcomings of traditional economic models and how integrating uncertainty can improve policymaking. Farmer emphasizes the role of innovative data collection and machine learning in enhancing economic insights. The conversation also touches on climate change, highlighting how detailed data can drive effective action and promote sustainability, offering a fresh perspective on managing today's complex economic challenges.

Doyne Farmer, a professor at Oxford's Institute for New Economic Thinking and a pioneer in chaos theory, discusses the fascinating world of complexity economics. He explains how dynamic models can reveal internal economic cycles instead of relying solely on external shocks. Farmer highlights the role of agent-based simulations in addressing crises like COVID-19 and explores innovative solutions for climate change. He also delves into the future of energy, weighing the pros and cons of nuclear power versus renewables in driving economic growth.

Complex systems scientist Doyne Farmer challenges traditional economics models, advocating for complex systems thinking and agent-based modeling for better predictions. Topics include limitations of standard models, consequences of inadequate models, leveraging alternative modeling techniques in business, and how CEOs can embrace complexity thinking.