In this hour-long virtual discussion with SFI’s Applied Complexity Network, J. Doyne Farmer explores early results from his group's efforts to adapt their computational models to account for the impact of COVID-19. These ACtioN conversations are by invitation, and typically follow the Chatham House Rule. However, because of the importance of this topic, and in agreement with the speaker, this video is being made publicly available.
The abstract as written by Doyne:
Social distancing measures in response to the COVID-19 pandemic constitute a unique natural experiment, in which we are shutting major parts of the economy down, hoping that we can restart it later. Can we predict what will happen? We began by modeling the shocks to the economy caused by social distancing. We construct a Remote Labor Index at the occupational level and map it onto industries, and combine this with a list of essential industries to estimate the shocks on the supply side. We then compare to the demand shocks to industries like airlines and restaurants, and feed the two into a supply chain model to predict how the shocks will reverberate through the economy. The shocks are large!
Over the long term, the COVID-19 pandemic underlines the need for complexity economics models that contain more realism. A good starting point would be an accurate model for the dynamics of supply chains. We have begun an effort to do this at the firm level. I present a vision of how we can gather the data and develop the necessary models, and discuss how this would be valuable for solving not just COVID-19, but also many of the economic challenges we are likely to face in the future.
Doyne has suggested that participants may benefit from reading this paper, his team's first on the topic. You can find more SFI work related to the pandemic on our Transmissions page.
ABOUT J. DOYNE FARMER:
Doyne Farmer is a member of SFI’s External Faculty, is the Baillie Gifford Professor at Mathematical Institute at the University of Oxford, and the Director of Complexity Economics at the Institute for New Economic Thinking at the Oxford Martin School. From 1999 to 2012 he led SFI’s economics program as a member of the resident faculty. His current research projects include Financial System Stability, Risk and Resilience, Economic Growth and Innovation, Technology and the Economy and Future of Capitalism.
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 spans complex systems, dynamical systems, time series analysis and theoretical biology. He founded the Complex Systems Group at Los Alamos National Laboratory, and while a graduate student in the 70’s he build the first wearable digital computer, which was successfully used to predict the game of roulette.