MIT System Dynamics Seminar | The remarkable universality of technology growth suggests that the green energy transition will happen quickly

Please visit the MIT System Dynamics Seminars page for more information.

You are invited to attend the System Dynamics Seminar being held on Friday, April 5^th from 12:30-2:00pm EST in the Jay W. Forrester conference room, E62-450, or via Zoom: https://mit.zoom.us/j/94114971874 (Password: SDSP24). Our virtual guest speaker will be J. Doyne Farmer (Oxford Martin School) presenting The remarkable universality of technology growth suggests that the green energy transition will happen quickly (see abstract and brief bio below; announcement attached). Lunch will be provided to those attending in person and a reminder email will be sent out closer to the date.

If you would also like to schedule a 30-minute 1:1 meeting with him before or after the seminar, please fill out the following link by COB Friday, March 29^th and I will confirm times and location with a calendar invite: https://rallly.co/invite/sVOs8NoOgYkn. Please notify me if you need to meet over Zoom instead.

Abstract

How fast will the green energy transition happen? To address this question we assembled a database on the deployment of 42 technologies, ranging from railroads to the internet.  When the individual time series are rescaled to have the same rates and levels, they have a universal form that is very close to a standard logistic S-curve. Although each technology’s rate of deployment varies due to many factors, the universal S-curve explains most of their behavior. We show that S-curve time series present challenges including autocorrelation, heteroscedastic noise and parameter bias, and develop a probabilistic method for forecasting deployment that takes these into account. Application to the time series for wind and solar energy suggest that we have still not reached rates of maximum deployment, and that the green energy transition is likely to happen surprisingly quickly.

Brief Bio

J. Doyne Farmer is Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford and an External Professor at the Santa Fe Institute. His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. He was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student he built the first wearable digital computer, which was successfully used to predict the game of roulette