Journal Articles

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), forthcoming, Technological Forecasting and Social Change, 2018. arxiv, Publisher, data.

We test and apply a simple method to make distributional forecasts of 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.

Self-organisation of knowledge economies, Journal of Economic Dynamics and Control 52, 150-165, 2015. Publisher

The structure of agents-ideas networks depends on the innovation/diffusion trade-off.

Working papers

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). arXiv ssrn, under review

When there are increasing returns to investment, there may be multiple locally optimum diversified portfolios.

Long-run dynamics of the U.S. patent classification system (with Daniel Kim). arXiv ssrnrevision resubmitted.

Patent classification systems change frequently and reflect the dynamics of technological change

Early identification of important patents through citation network centrality, with Manuel Sebastian Mariani and Matúš Medo, revision resubmitted.

We show how a citation network-based indicator, time rescaled PageRank, can be used to detect important patents a few years only after that they are granted.

Work in progress

The effect of experience on costs: Evidence from 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 to faster productivity improvements.

Knowledge diffusion and the structure of citations networks, under revision

The power law distribution of scientific citations may be explained by the social diffusion of knowledge

PhD Thesis


The evolution of knowledge systems, 2014, Maastricht University Press. PDF

Old 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”.