Modelling Economic Complexity: Insights for Risk Professionals

Hear from Prof. J. Doyne Farmer, Professor of Complex Systems Science at the University of Oxford, as we explore new modelling approaches designed to better capture the complex and chaotic nature of our climate and economy.

We spend a lot of time on this podcast covering the transition to a low carbon economy, which will be driven largely by policies and technological innovation. These policies tend to be based on insights from economics. And our view on the pace of innovation is often informed by expert judgement. But traditional economic models often oversimplify the world, leading to poor policy design. And we tend to underestimate the exponential rate of technological change, making us unduly pessimistic about the transition. 

Today’s guest has thought a great deal about both these issues. That’s why in today’s episode we’ll be diving into the world of complexity economics and agent-based modelling, which can help us better navigate the risks and opportunities associated with the transition. We’ll discuss:

• How agent-based models are very well suited to modelling complex, non-linear systems, such as the economy;
  
  
• How past innovation cycles can provide invaluable insights on what we might expect to see in the transition; and
  
  
• What the models tell us about the appropriate speed of the transition to a net zero world.

To find out more about the Sustainability and Climate Risk (SCR®) Certificate, follow this link: https://www.garp.org/scr

For more information on climate risk, visit GARP’s Global Sustainability and Climate Risk Resource Center: https://www.garp.org/sustainability-climate

If you have any questions, thoughts, or feedback regarding this podcast series, we would love to hear from you at: [e-mail]

Links from today’s discussion:

• Making Sense of Chaos: A Better Economics for a Better World: https://www.penguin.co.uk/books/284357/making-sense-of-chaos-by-farmer-j-doyne/9780241201978
• Santa Fe Institute’s Office of Applied Complexity: https://www.santafe.edu/applied-complexity/office
• GARP Climate Risk Podcast with Simon Sharpe: https://www.garp.org/podcast/five-times-faster-cr-240321
• GARP Climate Risk Podcast with David Stainforth: https://www.garp.org/podcast/predicting-climate-future-cr-241128

Speaker’s Bio(s)

Prof. J. Doyne Farmer, Professor of Complex Systems Science, University of Oxford

J. Doyne Farmer is Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment and Director of the Complexity Economics programme at the Institute for New Economic Thinking University of Oxford. He is also External Professor at the Santa Fe Institute and Chief Scientist at Macrocosm.

His current research is in economics, including agent-based modelling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. His book, Making Sense of Chaos: A Better Economics for a Better World, was published in 2024.

During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s he built the first wearable digital computer, which was successfully used to predict the game of roulette.