@inproceedings{33459,
  keywords = {technology, Experience curves, innovation, Technological innovation, Hindcasting, Learning curves, Performance curve, Production models, Technological improvements, Technological progress, Technology evolution, Technology improvement, Upper Bound, Commerce, Technological forecasting},
  author = {A.G Bailey and Q.M Bui and J.D Farmer and R.M Margolis and Ramamoorthy Ramesh},
  title = {Forecasting technological innovation},
  abstract = {Using a database of sixty-two different technologies, we study the issue of forecasting technological progress. We do so using the following methodology: pretending to be at a given time in the past, we forecast technology prices for years up to present day. Since our forecasts are in the past, we refer to it as hindcasting and analyze the predictions relative to what happened historically. We use hindcasting to evaluate a variety of different hypotheses for technological improvement. Our results indicate that forecasts using production are better than those using time. This conclusion is robust when analyzing randomly chosen subsets of our technology database. We then turn to investigating the interdependence of revenue and technological progress. We derive analytically an upper bound to the rate of technology improvement given the condition of increasing revenue and show empirically that all technologies fall within our derived bound. Our results suggest the observed advantage of using production models for forecasting is due in part to the direct relationship between production and revenue. © 2012 Gesellschaft fuer Informatk.},
  year = {2012},
  journal = {ARCS Workshops, ARCS 2012},
  isbn = {9781467319133},
  note = {cited By 2},
  language = {eng},
}