Research by INET Oxford's Prof Doyne Farmer & Francois Lafond has been used in an article on Bloomberg view. The article discusses President Trump's recent withdrawal from the Paris Climate agreement and his reasons for this. These reasons are then reflected against the work of Doyne & Francois.
Read the full article here.
Below is the abstract and the full paper can be accessed here.
Recently it has become clear that many technologies follow a generalized version of Moore’s law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore’s law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution. This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology at a given point in the future.