The future of technological progress
Forecasting the rate of technological progress by harnessing empirical regularities captured by Moore’s law and Wright’s law.
Technological progress is widely acknowledged as the main driver of economic growth, so any method for forecasting technological change is potentially very useful. But given that technological progress depends on innovation—generally thought of as something new and unanticipated—forecasting it might seem to be an oxymoron. In fact there are several postulated laws for technological improvement, such as Moore’s law and Wright’s law, but these are technology-dependent and we do not know how confident to be in their predictions.
In this project we investigate how to combine the forecasts of many technologies to make reliable distributional forecasts for a given technology. We use network theory and time series analysis to analyse performance curves and the large historical records of patenting activity to map the progress of technological ecosystems into the future.
The ability to forecast technological change with specified uncertainties can provide a foundation for a theory of economic growth. It can also offer clear recommendations for sustainability investments, where long-term planning is key. From a policy perspective, our insights provide an objective point of comparison to expert forecasts, which can be biased by vested interests.
Forecast errors for simple experience curve models facilitate more reliable estimates for the costs of technology deployment.
A formulation of Moore’s law estimates the probability that a given technology will outperform another at a certain point in the future.
The Yule-Simon distribution describes the diffusion of knowledge and ideas in a social network which in turn influences economic growth.