Some of the results were surprising, says Jessika Trancik, an assistant professor of engineering systems at MIT. The findings could help industries to assess where to focus their research efforts, investors to pick high-growth sectors, and regulators to more accurately predict the economic impacts of policy changes.
The analysis indicates that Moore’s Law is one of two formulas that best match actual technological progress over past decades. The top performer, called Wright’s Law, was first formulated in 1936: It holds that progress increases with experience — specifically, that each percent increase in cumulative production in a given industry results in a fixed percentage improvement in production efficiency.
To carry out the analysis, the researchers amassed an extensive set of data on actual costs and production levels over time for 62 different industry sectors; these ranged from commodities such as aluminum, manganese and beer to more advanced products like computers, communications systems, solar cells, aircraft and cars.
“There are lots of proposals out there,” Trancik says, for predicting the rate of advances in technologies. “But the data to test the hypotheses is hard to come by.”
The rates of change vary greatly among different technologies, the team found.
“Information technologies improve the fastest,” Trancik says, “but you also see the sustained exponential improvement in many energy technologies. Photovoltaics improve very quickly. … One of our main interests is in examining the data to gain insight into how we can accelerate the improvement of technology.”
PLOS One - Statistical Basis for Predicting Technological Progress
ABSTRACT - Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly the same. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation.
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