Funding and Management

The workshop attendees and committee believe that the forecasting system should be built and funded according to a start-up model, whereby the government provides seed-level financing to build a working 1.0 version of the system. Beginning the project with minimal resources forces the development team to make tough decisions up front and to focus the effort on developing and perfecting core system features. The leaders in charge of the system should be forced to seek additional outside funding sources, ensuring that the system is robust enough in its early stages to inspire confidence and attract sponsorship. Once developed, the system needs to be able to sustain itself by providing enough ongoing value to attract continual sponsorship from both government and other parties (including governments, corporations, institutions, and organizations) to cover the cost of operating, maintaining, and improving the system.

Key Recommendation. The Department of Defense and the intelligence community should begin the process of building a persistent forecasting system by selecting leadership and a small, independent, development team. The team should be given seed-level funding to establish an organizational structure and business plan and build a working 1.0 version of a disruptive technology forecasting system. The organization should have to attract additional funds from domestic and foreign corporate, nonprofit, or government sources. (Recommendation 3-6)


Bhan, Nita. 2010. Emerging markets as a source of disruptive innovation: 5 case studies. Core 77 Design Magazine and Resource. February 3. Available at Last accessed March 1, 2010.

Bower, Joseph L., and Clayton M. Christensen. 1995. Disruptive technologies: Catching the wave. Harvard Business Review. January-February.

Dalkey, Norman C. 1967. DELPHI. Santa Monica, Calif.: RAND Corporation.

Enis, C.R. 1995. Expert-novice judgments and new cue sets: Process versus outcome. Journal of Economic Psychology 16(4): 641-662.

Giridharadas, Anand. 2008. The making of Tata’s new car. New York Times. January 7. Available at Last accessed March 1, 2010.

Govindarajan, Vijay. 2009. The case for “reverse innovation” now. Business Week. October 26. Available at Last accessed March 1, 2010.

Howe, Jeff. 2006. The Rise of Crowdsourcing. Wired Magazine. June 14. Available at Last accessed May 27, 2010.

Johnston, Rob. 2003. Reducing analytic error: Integrating methodologists into teams of substantive experts. Studies in Intelligence 47(1): 57-65. Available at Last accessed May 8, 2009.

NRC (National Research Council). 2010. Persistent Forecasting of Disruptive Technologies. Washington, D.C.: The National Academies Press.

Önkal, D., J.F. Yates, C. Simga-Mugan, and S. Öztin. 2003. Professional vs. amateur judgment accuracy: The case of foreign exchange rates. Organizational Behavior and Human Decision Processes 91: 169-185.

Whitford, David. 2008. Hired guns on the cheap: New online services can help you find freelancers for less. Fortune Small Business Magazine. Available at Last accessed January 29, 2010.

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