Work

How well do experience curves predict technological progress? A method for making distributional forecasts

Year: 2017

Type: article

Source: Technological Forecasting and Social Change

DOI: 10.1016/j.techfore.2017.11.001

Authors: François Lafond, Aimee Gotway Bailey, Jan David Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, J. Doyne Farmer

Institutions: University of Oxford, London Institute for Mathematical Sciences, United States Department of Energy, Max Planck Institute for the Physics of Complex Systems, University of Rwanda, Carnegie Mellon University Africa, Carnegie Mellon University, Santa Fe Institute

Language: English

Topic: Innovation Diffusion and Forecasting

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: Institute for New Economic Thinking, European Commission, Horizon 2020 Framework Programme, FP7 Information and Communication Technologies

Awards: G7571406853, COP21: Results and Implications for Pathways and Policies for Low Emissions European Societies, GROWTH AND INNOVATION POLICY-MODELLING: APPLYING NEXT GENERATION TOOLS, DATA, AND ECONOMIC COMPLEXITY IDEAS

OpenAlex ID: W2597852060

FWCI: 4.847

Cites: 82

Cited by: 79

Related to: 10