Satellite: Quantitative Methods for Predicting, Explaining and Describing Technological Change

Wednesday, September 30, 2015
1:00 pm – 5:00 pm
Hilton DoubleTree:  Encantada I

Satellite Organizers

Giorgio Triulzi, Singapore University of Technology and Design/UNU-MERIT, Singapore
Jeff Alstott, Singapore University of Technology and Design, Singapore
HyeJin Youn, University of Oxford, UK/Santa Fe Institute, USA
François Lafond, LIMS/INET @Oxford Martin School/UNU-MERIT, Netherlands
Bowen Yan, Singapore University of Technology and Design, Singapore


Technology is a complex evolving system. The direction of technical improvements depends on the characteristics of the problems, the topology of their interactions, the strategic behavior of inventing agents and on market response. Therefore, understanding and, possibly, predicting technological change, no matter which dimension of change, requires collecting and analyzing a large variety of data and disentangling the complexity of nonlinear interactions among the determinants of technical change. This reduces our ability to understand the dynamics of technical change and poses serious challenges on the possibility of doing sound, empirically based forecasting and strategic planning. However, the availability of big data and new data mining techniques and the increase in computational power allowed scholars to push the frontier of what is possible in terms of description, explanation and prediction of technological change further. Moreover, considerable advances have been made on modelling technology dynamics. This session aims at bringing together scholars working on quantitative technology forecasting, data-driven analysis of the determinants of technical progress and models of complex technology dynamics. Examples of the approaches used includes, but are not limited to, network analysis and text mining of patent data, time series analysis of price and performance data, agent-based models of technical progress and models aiming at explaining technology push or demand-pull determinants of technical change and their effect on the economy.


The Chronometric Knowledge Frontier in Patenting and Law: Investigating Mid-Career of Inventors and Effect of Teams
Satyam Mukherjee, Northwestern University, USA
Ryan Whalen, Northwestern University, USA
Daniel Romero, University of Michigan, USA
Brian Uzzi, Kellogg School of Management, Northwestern University Evanston, USA
Benjamin Jones, Kellogg School of Management, Northwestern University Evanston, USA

Anatomy of Scientific Evolution Based on Millions of Digitized Books
Pan-Jun Kim, Asia Pacific Center for Theoretical Physics, The Republic of Korea

Three Empirical Facts on Biographical Collective Memory
Cristian Jara Figueroa, MIT Media Lab, USA
Amy Yu, MIT Media Lab, USA
Cesar Hidalgo, MIT Media Lab, USA

A Trophic Model of Economic Growth
James McNerney, MIT, USA
Charles Savoie, Oxford University, UK
Francesco Caravelli, Oxford University, Canada
Doyne Farmer, Oxford University, UK

Collaboration in Innovation: Firm Patent Networks and Technology Portfolios
Catriona Sissons, University of Auckland, New Zealand
Dion O’Neale, University of Auckland, New Zealand
Shaun Hendy, University of Auckland, New Zealand

The Local Structure of Worldwide Innovation Hubs
Greg Morrison, IMT Institute for Advanced Study, Lucca Italy, Italy
Orion Penner, IMT Lucca, Italy
Massimo Riccaboni, IMT Institute for Advanced Studies, Lucca, Italy
Fabio Pammolli, IMT Institute for Advanced Study, Lucca, Italy