The Universality and Predictability of Technology Diffusion

Explore the universality and predictability of technology diffusion in this lecture by Doyne Farmer from the University of Oxford. Delve into the S-curve patterns observed across 47 diverse technologies, from monasteries to mobile phones. Examine how the Gompertz function explains over half the variance in technology diffusion at peak growth, highlighting the similarities among different technological advancements. Investigate the challenges in analyzing technology S-curve time series, including nonstationarity, autocorrelation, and heteroscedastic noise. Learn about a developed time series model that addresses these issues and enables probabilistic forecasting of future deployment. Discover how forecasting accuracy varies based on the horizon and stage of development. Apply these insights to renewable energy transitions, particularly solar and wind, and understand their potential to rapidly displace fossil fuels within the next two decades.

The Universality and Predictability of Technology Diffusion