ABSTRACT
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.
Ref: How predictable is technological progress? Research Policy (April 2016) | DOI: 10.1016/j.respol.2015.11.001 | PDF (Open Access)
Full list (A to Z):
Acrylic Fiber
Acrylonitrile
Aluminum
Ammonia
Aniline
Automotive (US)
Beer (Japan)
Benzene
Bisphenol A
Caprolactam
Carbon Black
Concentrating Solar
Cyclohexane
DNA Sequencing
DRAM
Electric Range
Ethanolamine
Ethylene
Formaldehyde
Geothermal Electricity
Hard Disk Drive
Isopropyl Alcohol
Laser Diode
Low Density Polyethylene
MaleicAnhydride
Methanol
Milk (US)
Monochrome Television
Neoprene Rubber
Paraxylene
Pentaerythritol
Phenol
Photovoltaics
Phthalic Anhydride
Polyester Fiber
Polyethylene HD
Polyethylene LD
Polypropylene
Polystyrene
Polyvinylchloride
Primary Aluminum
Primary Magnesium
Sodium
Sodium Chlorate
Sorbitol
Styrene
Titanium Dioxide
Titanium Sponge
Transistor
Urea
Vinyl Acetate
Vinyl Chloride
Wind Turbine (Denmark)