# Moore's Law trumped by Wright's Law

PORTLAND, Ore.—When it comes to predicting the future, Moore's Law has been a time-worn bell-weather. But did you know its just one of several competing laws with names like Sinclair-Klepper-Cohen's, Goddard's and Wright's Law?

Recently researchers at Massachusetts Institute of Technology (MIT) c**ompared the accuracy of each competing law in both its short- and long-term predictions**. MIT claims their findings will improve the accuracy of future predictions about technological change, candidate technologies and policies for global change.

Overall the best long-term predictor is Wright's Law, which improves its accuracy over Moore's law by framing its horizon in terms of units-of-production instead of absolute time. For instance, Moore's Law predicts that every 18 months the density of semiconductors will double, whereas Wright's Law predicts that as the number of units manufactured increases the cost-of-production decreases (no matter how long that might take). Thus Wright's Law--named after aeronautical engineer, Theodore "T.P." Wright--offers more accurate long-term predictions since it automatically adapts to economic growth rates.

**Growth of prediction errors for competing laws to Moore's Law shows Wright's Law the best at long-time horizons, Goddard's Law as the worse at short time horizons, and Sinclair-Klepper-Cohen the worst for long-time horizons.**

Wright's and other alternatives, such as Goddard's (which postulates that progress is driven only by economies of scale) and Sinclair-Klepper-Cohen's (which combines Wright's and Goddard's), were compared to the actual cost and production units in 62 different technologies, including computers, communications systems, solar cells, aircraft and automobiles. Historical data allowed accurate comparisons using "hind-casting" whereby a statistical model was developed to rank the performance of each postulated law over time.

MIT claims its results show that with careful use of historical data, future technological progress is forecastable with a typical accuracy of about 2.5 percent per year. The research was conducted by MIT professor Jessika Trancik, professor Bela Nagy at the Santa Fe Institute, professor Doyne Farmer at the University of Oxford and and professor Quan Bui at St. JohnŐs College (Santa Fe, N.M.).

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