FIGURE 1-1 Curve showing the distribution of notional frequencies of citation for individual technologies.

FIGURE 1-1 Curve showing the distribution of notional frequencies of citation for individual technologies.

be captured by traditional systems. The intent of this report is to develop a persistent forecasting system that can adequately identify the impact of technologies that are located in the tail of the distribution, on the far right side of the plot. A new, highly disruptive technology may be in this long tail either (1) because the magnitude of potential change it could produce is not appreciated and thus it is rarely cited or (2) because the market for the new technology is not obvious. The shift of aircraft propulsion from propeller to jet engine is an example of the first, while the rapid growth of the World Wide Web is an example of the second.

The challenge then becomes identifying potentially disruptive technologies in a sea of new technology innovations, applications, and discoveries. Compounding this challenge is the fact that some of the most disruptive technologies may emerge where no threat previously was known or even suspected, and that the ultimate impact may be the result of an integration of multiple existing technologies to create a new, highly disruptive application. These factors make it difficult for forecasters to determine important precursor signals of certain classes of disruptive technologies. New techniques and tools such as backcasting, contextual database searches, social networking analytical tools, interactive online gaming methodologies, alternative reality gaming, predictive markets, expected returns theory, portfolio and venture strategies, and visualization systems could improve signal development and identification.


As the world becomes more interconnected, small changes in one arena can trigger significant disruptions in others. Furthermore, decision makers in government, corporations, and institutions are faced with shrinking time frames in which to plan and react to disruptions. Traditional methodologies for forecasting disruptive technologies are generally incapable of predicting the most extreme scenarios, some of which may lead to the most potentially beneficial or catastrophic events. The committee believes the convergence of a number of advances—the increasing ubiquity of the Internet, the improving cost-efficiency of data storage and communications, the growing power of computation processing, and the globalization of trade and knowledge—has produced new tools and methods for forecasting emerging technologies that will bring about disruptions.

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