PERSISTENT FORECASTING OF DISRUPTIVE TECHNOLOGIES – REPORT 2
NATIONAL RESEARCH COUNCIL
OF THE NATIONAL ACADEMIES
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NOTICE: The project that is the subject of this report was approved by the Governing Board of the National Research Council, whose members are drawn from the councils of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance.
This is a report of work supported by contract No. HHM40205D0011 between the Defense Intelligence Agency and the National Academy of Sciences. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the organizations or agencies that provided support for the project.
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THE NATIONAL ACADEMIES
Advisers to the Nation on Science, Engineering, and Medicine
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COMMITTEE ON FORECASTING FUTURE DISRUPTIVE TECHNOLOGIES
GILMAN G. LOUIE, Chair,
Alsop Louie Partners, San Francisco
BBN Technologies, Cambridge, Massachusetts
HARRY BLOUNT, DISCERN,
RUTH A. DAVID,
Analytic Services, Inc (ANSER), Arlington, Virginia
Drew Solutions, Inc., Summit, New Jersey
University of Maryland, College Park
Case Western Reserve University, Cleveland, Ohio
JENNIE S. HWANG,
H-Technologies Group, Cleveland, Ohio
ANTHONY K. HYDER,
Univerity of Notre Dame, Notre Dame, Indiana
Elmarco, Inc., Chapel Hill, North Carolina
Saffo.com, Burlingame, California
Global Business Network, San Francisco
Sandia National Laboratories, Albuquerque, New Mexico
ALFONSO VELOSA III,
Gartner, Inc., Tucson, Arizona
NORMAN D. WINARSKY,
SRI International, Menlo Park, California
MICHAEL A. CLARKE, Lead DEPS Board Director
DANIEL E.J. TALMAGE, JR., Study Director
KAMARA BROWN, Research Associate
SARAH CAPOTE, Research Associate
SHANNON THOMAS, Program Associate
Monitoring and harnessing the power of global technological innovation are necessary tasks for any nation that seeks to promote the well-being and safety of its citizens. Globally interconnected business, financial, social, and political networks connect more people than ever before to the positive and negative disruptive impacts of novel uses of technology. The increased spread of knowledge and opportunity throughout the world has been accompanied by an increase in technological innovation, particularly from smaller organizations and nontraditional sectors. The purpose of a forecasting system for disruptive technologies is to minimize surprise related to disruptive innovation and to prepare decision makers for the future. To assess current forecasting methods and aid in the development of a next-generation forecasting system, the Defense Warning Office of the Defense Intelligence Agency and the Director of Defense Research and Engineering requested that the National Research Council (NRC) establish the Committee on Forecasting Future Disruptive Technologies.
This is the second of the two reports requested by the sponsoring organizations. In its first report (National Research Council, Persistent Forecasting of Disruptive Technologies, The National Academies Press, Washington, D.C., 2010), the committee defines “disruptive technology,” analyzes existing forecasting strategies and methods, and discusses in detail the characteristics of a long-term persistent forecasting system. In this report, the committee attempts to create a model for a buildable forecasting system incorporating many of the methods and characteristics outlined in the first report.
As chair, I wish to express appreciation to the members of this committee for their earnest contributions to the generation of this report. The members are grateful for the interest and assistance of many members of the technology and forecasting community, as well as to the sponsors for their support. The committee would also like to express sincere appreciation for the support and assistance of NRC staff members Michael Clarke, Daniel Talmage, Kamara Brown, Sarah Capote, and Shannon Thomas; Christine Mirzayan Science and Technology Policy Fellow Sarah Lovell; and technical writer Linda Voss.
Gilman G. Louie, Chair
Committee on Forecasting Future Disruptive Technologies
Acknowledgment of Reviewers
This report has been reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise, in accordance with procedures approved by the National Research Council’s (NRC’s) Report Review Committee. The purpose of this independent review is to provide candid and critical comments that will assist the institution in making its published report as sound as possible and to ensure that the report meets institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We wish to thank the following individuals for their review of this report:
Andrew Brown, Jr., NAE, Delphi Corporation,
Natalie W. Crawford, NAE, The RAND Corporation,
Alexander H. Flax, NAE, Potomac, Maryland,
Brig Gen Allison Hickey, USAF (Ret.), Accenture National Security Services,
Thom J. Hodgson, NAE, North Carolina State University,
Darrell Long, University of California, Santa Cruz,
Christopher L. Magee, NAE, Massachusetts Institute of Technology,
Raghunath A. Mashelkar, NAS/NAE, National Chemical Laboratory, and
Ray Strong, IBM Almaden Research Center.
Although the reviewers listed above have provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations, nor did they see the final draft of the report before its release. The review of this report was overseen by Maxine Savitz (NAE), Honeywell (Ret.), and Michael Zyda, University of Southern California. Appointed by the NRC, they were responsible for making certain that an independent examination of this report was carried out in accordance with institutional procedures and that all review comments were carefully considered. Responsibility for the final content of this report rests entirely with the authoring committee and the institution.
ARG alternative reality game
DDR&E Director of Defense Research and Engineering
DIA Defense Intelligence Agency
DoD Department of Defense
DWO Defense Warning Office
IC intelligence community
IED improvised explosive device
IT information technology
NIC National Intelligence Council
NRC National Research Council
QDR Quadrennial Defense Review
TED Technology, Entertainment, and Design
1.0 system A minimal working system with basic core functions that is used by its target users as a production system. A 1.0 system is different from a prototype, which typically is a testbed used to validate an approach with a small number of users and has a subset of the required core functions. The label “1.0” is typically given to the first fully working product that is released to its target users.
accountable prediction A forecast with a specified end date and a testable, wagerable proposition.1
backcasting Exploring a projected future scenario for potential paths that could lead from the present to the forecast future. This can include the identification of signposts and signals that indicate the accuracy of a prediction.
back testing Evaluating an event that has already occurred to validate a forecasting methodology.
closed ignorance Information is available, but stakeholders are unwilling or unable to consider that some outcomes are unknown.
cloud computing A software model that uses remote servers to provide real-time delivery of services to customers by the Internet.
community of interest A group or collection of people trying to solve a common problem or having a shared concern.
crowdsourcing The act of outsourcing to the public or a selected subset of the public a function previously performed by employees.
data hygiene Principles and practices for removing errors and repetition from data in a database.
Definition adapted from http://lewisshepherd.wordpress.com/2008/05/25/is-it-even-possible-to-connect-the-dots./. Last accessed January 28, 2010.
data mining/data harvesting An automated process of extracting patterns from data.
Delphi method A structured approach to eliciting forecasts from groups of experts, with an emphasis on producing an informed consensus view of the most probable future.
disruptive technology Innovative technology that triggers sudden and unexpected effects. The term was first coined by Bower and Christensen in 19952 to refer to a type of technology that brings about a sudden change to established technologies and markets. Because these technologies are characteristically hard to predict and occur infrequently, they are difficult to identify or foresee.
distribution analysis A statistical method of analysis that can be used to describe the relationship between items in a data set, or to predict the probability of future occurrence of a data point.
enabler Technology that makes possible one or more technologies, processes, or applications.
exceptions analysis A method of analysis that uses algorithms to determine when a data point goes beyond a “normal” threshold.
expert sourcing Working with a specialized group of experts to solve a problem.
extrapolation The use of techniques such as trend analyses and learning curves to generate forecasts.
gear down Using archaic technologies to solve current problems.
gear up Applying scientific advances to create advanced technology to solve problems.
innovation The creation of a new device or process as a result of study and/or experimentation.
long bet See accountable prediction.
mash, mashup A Web page or application that combines data or functionality from two or more external sources to create a new service.3
measurement of interest A key characteristic or indicator that can be monitored to anticipate the development of disruptive technologies and applications. A measurement of interest could have a threshold (e.g., energy stored per unit of mass, price per gallon of gasoline) that, once crossed, triggers other significant occurrences. Such a threshold on a measurement of interest could provide a signal or signpost.
mining See data mining.
narrative A story or an account of events or experiences, either true or fictitious. In a forecast, a narrative can provide a context within which a specific prediction takes on broader significance.
persistent forecast A forecast that is continually improved as new methodologies, techniques, or data become available.
Joseph L. Bower and Clayton M. Christensen. 1995. Disruptive technologies: Catching the wave. Harvard Business Review. January-February.
From http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid). Last accessed November 17, 2009.
prediction market A market created for the purpose of making predictions on future events (e.g., presidential elections).
roadmapping A time-honored technique for forecasting technological advances. It is most useful for forecasting raw technical capabilities, not for forecasting the applications enabled by technologies.
scenario See also vision. An imagined or projected sequence of events. Scenarios can be used as tools for understanding the complex interaction of forces that influence future events.
signal A piece of data, a sign, or an event that is relevant to the identification of a potentially disruptive technology: for example, Apple, Inc., placing a large order for new touch capacitance screens from a Chinese supplier.
signpost A recognizable and actionable potential future event that could indicate an upcoming disruption. “Recognizable” means that reasonable people would agree on whether the event has happened. “Actionable” means that the event is sufficiently important to require an organizational decision and response.
technology forecasting system Technologies, people, and processes assembled to minimize surprise triggered by emerging or disruptive technologies, in order to support decision making.
tipping point The time at which the momentum for change becomes unstoppable.
trend extrapolation A forecasting method in which data sets are analyzed to identify trends that can provide predictive capability.
viral Pertaining to the rapid spread or distribution of an idea from one to many.
vision A forecast of a potential future state of reality described in a vague way: for example, passenger vehicles powered primarily by energy sources other than the gasoline-powered internal combustion engine. See also scenario.
Web crawling or spidering A process in which a computer program methodically searches the Internet for specific types of data.
Web scraping A computer software technique of extracting information from Web sites.4
wiki A community-accessible Web site with a user-friendly graphic user interface that can be used for collaborative work on documents.
For more information, see http://www.extractingdata.com/web%20scraping.htm. Last accessed January 28, 2010.