Being able to predict the pace of technological development could be quite useful for a lot of people. No surprise then, that several models (or ‘laws’) have been posited that aim to describe how technological progress will unfurl (the most famous one probably being Moore’s law, for those interested: original article here).
However, these laws or models haven’t really been tested in a broader perspective.
Now, they have. A new article in PLOS ONE evaluates six models for predicting technological progress, namely:
- Wright’s law (1936): the cost decreases at a rate that depends on cumulative production.
- Moore’s law (generalized, 1965): the cost of a given technology decreases exponentially with time.
- Goddard’s model (1982): progress is driven purely by economies of scale.
- SCK model (Sinclair, Klepper and Cohen, 2000): a combination of Wrights’ and Goddard’s law.
- Nordhaus’ model (2009): a combination of Moore’s and Wright’s law.
- The sixth hypothesis the researchers consider, is Wright’s law lagged by one year.
Next, the ability of these hypotheses to predict technological progress was tested using a database holding data concerning the cost and production of 62 technologies, for annual intervals over a duration of 10 to 39 years.
Can’t go wrong with old school: Wright’s law, with Moore’s law a close second.
In fact, an exponential increase in production and exponential decrease in cost make both laws indistinguishable (over long time spans, however, Moore’s law could perform significantly worse than the one postulated by Wright).
Interestingly, these laws hold even for technologies they weren’t originally proposed for (Wright: airplanes, Moore: integrated circuits), such as the production of chemicals and photovoltaic cells.
But, as the authors note:
Of course we must add the usual caveats about making forecasts – as Niels Bohr reputedly said, prediction is very difficult, especially of the future.
They also stress that:
Our primary goal in this paper is to compare the performance of proposed models in the literature for describing the cost evolution of technologies. Our objective is not to construct the best possible forecasting model.
And, as is noted in this Nature piece concerning the article, it’s unclear whether these laws are able to predict the development of young technologies. After all, the technologies in the database are ‘those that survived’. It also stresses that other factors, such as governmental policies play a role. Finally, the unpredictable development of new technologies, following an unexpected breakthrough, can thoroughly shake the technology landscape, like ripples in a calm pond, messing up previous forecasts.
While forecasting and anticipating is important and often educational, and well-founded guesses and probability estimates can be made, actually predicting the future, it seems, is, with our current knowledge, still tricky business.
Nagy, B., Farmer, J., Bui, Q., & Trancik, J. (2013). Statistical Basis for Predicting Technological Progress PLoS ONE, 8 (2) DOI: 10.1371/journal.pone.0052669