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

F. Lafond, A. Bailey, J. Bakker, D. Rebois, R. Zadourian, P. McSharry, D. Farmer

Technological Forecasting and Social Change 128, 104 (2018)

#technology#innovation#forecasting

Download the PDF

LQ placeholderWe presented a method to test the accuracy and validity of experience curve forecasts.

We presented a method to test the accuracy and validity of experience curve forecasts.

Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 51 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially. To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules.

LQ placeholder

DebtRank: a microscopic foundation for shock propagation

M. Bardoscia, S. Battiston, F. Caccioli, G. Caldarelli

PLOS ONE

LQ placeholder

Self-organization of knowledge economies

F. Lafond

Journal of Economic Dynamics and Control

LQ placeholder

Credit default swaps networks and systemic risk

M. Puliga, G. Caldarelli, S. Battiston

Scientific Reports

LQ placeholder

The rise of China in the international trade network: a community core detection approach

Z. Zhu, F. Cerina, A. Chessa, G. Caldarelli, M. Riccaboni

PLoS ONE

5 / 123 papers