Can stimulating demand drive costs down? World War II as a natural experiment (with Diana Greenwald and J. Doyne Farmer)
During WWII, the demand for weapons was not driven by prices. Yet, to some extent faster growth of experience was associated with faster productivity improvements.
Automation and occupational mobility: a data-driven network model (with R. Maria del Rio-Chanona, Penny Mealy, Mariano Beguerisse, J. Doyne Farmer), arXiv 1906.04086.
We develop a labor market model based on the occupational mobility network, and use it to assess the effects of the next wave of automation.
Why is productivity slowing down? (with Ian Goldin, Pantelis Koutroumpis, Nils Rochowicz and Julian Winkler), under revision
We comprehensively review the various explanations for the current productivity slowdown
Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves (with Rupert Way, Valentyn Panchenko, Fabrizio Lillo, and J. Doyne Farmer), Journal of Economic Dynamics & Control 101, pp. 211-238, 2019, Open access at publisher
When there are increasing returns to investment, there may be multiple locally optimal diversified portfolios.
Long-run dynamics of the U.S. patent classification system (with Daniel Kim). Journal of Evolutionary Economics 29(2), pp. 631–664, 2019, Open access at Publisher, Data.
Patent classification systems change frequently and reflect the dynamics of technological change
Early identification of important patents: design and validation of citation network metrics, with Manuel Mariani and Matúš Medo, Technological Forecasting and Social Change, forthcoming. Publisher.
A citation network-based indicator, time rescaled PageRank, can be used to detect important patents a few years only after that they are granted.
How well do experience curves predict technological progress? A method for making distributional forecasts (with Aimee G. Bailey, Jan D. Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, and J. Doyne Farmer), Technological Forecasting and Social Change 128, pp 104-117, 2018. arXiv, Publisher, Data.
We test and apply a simple method to make distributional forecasts for technological progress conditional on the growth of experience.
How predictable is technological progress? (with J. Doyne Farmer), Research Policy 45(3), 647–665, 2016. Open access at Publisher, Data.
We test and apply a simple method to make distributional forecasts for technological progress.
Advanced work in progress
Disruptive technologies and regional innovation policy (with Pantelis Koutroumpis)
Origins and evolution of technological domains (with Vilhelm Verendel and J. Doyne Farmer)
Synchronization of innovation dynamics in the technological ecosystem, with Anton Pichler and J. Doyne Farmer
The evolution of knowledge systems, 2014, Maastricht University Press. PDF
Resting working papers
The size of patent categories: USPTO 1976-2006 (2014), UNU-MERIT WP #2014-060. Updated as “Long-run dynamics of the U.S. patent classification system”, with Daniel Kim.
Learning and the structure of citations networks (2012), UNU-MERIT WP #2012-071. Partly published as “Self-organization of knowledge economies”, and updated as “Knowledge diffusion and the structure of citations networks”, 2014.