PERSISTENT FORECASTING OF DISRUPTIVE TECHNOLOGIES
THE NATIONAL ACADEMIES PRESS
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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 view of the organizations or agencies that provided support for the project.
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COMMITTEE ON FORECASTING FUTURE DISRUPTIVE TECHNOLOGIES
GILMAN G. LOUIE, Chair,
Alsop Louie Partners, San Francisco
BBN Technologies, Cambridge, Massachusetts
Blount Ventures, Hillsborough, California
RUTH A. DAVID,
ANSER, Arlington, Virginia
Drew Solutions, Inc., Summit, New Jersey
University of Maryland, College Park
JENNIE S. HWANG,
H-Technologies Group, Cleveland, Ohio
ANTHONY K. HYDER,
University of 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., Tuscon, Arizona
MICHAEL A. CLARKE, Lead DEPS Board Director
DANIEL E.J. TALMAGE, JR., Study Director
LISA COCKRELL, Mirzayan Policy Fellow, Senior Program Associate (until 8/10/2009)
ERIN FITZGERALD, Mirzayan Policy Fellow, Senior Program Associate (until 8/14/2009)
KAMARA BROWN, Research Associate
SARAH CAPOTE, Research Associate
SHANNON THOMAS, Program Associate
Technological innovations are key causal agents of surprise and disruption. These innovations, and the disruption they produce, have the potential to affect people and societies and therefore government policy, especially policy related to national security. Because the innovations can come from many sectors, they are difficult to predict and prepare for. The purpose of predicting technology is to minimize or eliminate this surprise. To aid in the development of forecasting methodologies and strategies, the Committee on Forecasting Future Disruptive Technologies of the National Research Council (NRC) was funded by the Director, Defense Research and Engineering (DDR&E) and the Defense Intelligence Agency’s (DIA’s) Defense Warning Office (DWO) to provide an analysis of disruptive technologies.
This is the first of two planned reports. In it, the committee describes disruptive technology, analyzes existing forecasting strategies, and discusses the generation of technology forecasts, specifically the design and characteristics of a long-term forecasting platform. In the second report, the committee will develop a hybrid forecasting method tailored to the needs of the sponsors.
As chairman, I wish to express our appreciation to the members of this committee for their earnest contributions to the generation of this first report. The members are grateful for the active participation of many members of the technology 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 the NRC staff, including Michael Clarke, Daniel Talmage, Lisa Cockrell, Erin Fitzgerald, Kamara Brown, Sarah Capote, Carter Ford, and Shannon Thomas.
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 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:
Peter M. Banks, NAE, Astrolabe Ventures,
Andrew Brown, Jr., NAE, Delphi Corporation,
Natalie W. Crawford, NAE, RAND Corporation,
Thom J. Hodgson, NAE, North Carolina State University,
Anita K. Jones, NAE, University of Virginia,
Julie J. C. H. Ryan, George Washington University,
Kenneth W. Wachter, NAS, University of California, Berkeley, and
Ruoyi Zhou, 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 (retired). Appointed by the NRC, she was 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.
Acronyms and Abbreviations
ARG alternate reality games
BOINC Berkeley Open Infrastructure for Network Computing
CAS complex adaptive system
DARPA Defense Advanced Research Projects Agency
DDR&E Director, Defense Research and Engineering
DNA deoxyribonucleic acid
DoD Department of Defense
DWO Defense Warning Office
EC2 elastic compute cloud
ETL extract, transform, and load
GDP gross domestic product
GPS Global Positioning System
GUI graphical user interface
HD high definition
IC intelligence community
IED improvised explosive device
IEEE Institute of Electrical and Electronics Engineers
IFTF Institute for the Future
MCF meta content framework
MEMS microelectromechanical systems
MMORPG massive multiplayer online role-playing game
NaCTeM National Center for Text Mining
NASA National Aeronautics and Space Administration
NATO North Atlantic Treaty Organization
NGO nongovernmental organization
NORA Nonobvious Relationship Awareness
NRC National Research Council
NSF National Science Foundation
PC personal computer
PCR polymerase chain reaction
QDR quadrennial defense review
R&D research and development
RDB relational database
RDF resource description framework
S3 Simple Storage Service
SAS Statistical Analysis Software
SIMS School of Information Management and Systems, University of California at Berkeley
SMT simultaneous multithreading
TIGER Technology Insight–Gauge, Evaluate, and Review
T-REX The RDF Extractor, a text mining tool developed at the University of Maryland
TRIZ Rus: Teoriya Resheniya Izobretatelskikh Zadatch (“inventor’s problem-solving theory”)
U.S. United States
WWII World War Two
Backcasting Explores a future scenario for potential paths that could lead from the present to the forecast future.
Breakthrough Discovery or technology that changes a fundamental understanding of nature or makes possible something that previously seemed impossible (or improbable).
Catalyst Technology that alters the rate of change of a technical development or alters the rate of improvement of one or more technologies.
Chaos theory Characterizes deterministic randomness, which indeed exists in the initial stages of technology phase transition.
Delphi method Structured approach to eliciting forecasts from groups of experts, with an emphasis on producing an informed consensus view of the most probable future.
Disruption Event that significantly changes or interrupts movement or a process, trend, market, or social direction (Source: Dictionary.com).
Disruptive technology Innovative technology that triggers sudden and unexpected effects. The term was first coined by Bower and Christensen in 1995 to refer to a type of technology that brings about a sudden change to established technologies and markets (Bower and Christensen, 1995). Because these technologies are characteristically hard to predict and occur infrequently, they are difficult to identify or foresee.
Enhancer Technology that modifies existing technologies, allowing a measure of interest in the technologies to cross a critical threshold or tipping point.
Enabler Technology that makes possible one or more new technologies, processes, or applications.
Extrapolation Use of techniques such as trend analyses and learning curves to generate forecasts.
Forecasting bias Incompleteness in the data sets or methodologies used in a forecasting system (meaning in this report).
Genius forecast Forecast by a single expert who is asked to generate a prediction based on his or her intuition.
Ignorance Lack of knowledge or information. Ignorance contributes to bias in a forecast, which in turn can cause surprise.
Individual bias Prejudice held by a human being.
Influence diagram Compact graphical or mathematical representation of the decision-making process.
Intuitive view Opinion that the future is too complex to be adequately forecast using statistical techniques but should instead rely primarily on the opinions or judgment of experts.
Long-term forecasts Forecasts of the deep future (10 or more years from the present).
Measurement of interest Key characteristic that can be monitored to anticipate the development of disruptive technologies and applications.
Medium-term forecasts Forecasts of the intermediate future (typically 5 to 10 years from the present).
Morpher Technology that creates one or more new technologies when combined with another technology.
Persistent forecast Forecast that is continually improved as new methodologies, techniques, or data become available.
Scenario Tool for understanding the complex interaction of a variety of forces that can influence future events (meaning in this report).
Short-term forecasts Forecasts that focus on the near future (5 years or less from the present).
Signal Piece of data, a sign, or an event that is relevant to the identification of a potentially disruptive technology.
Signpost Recognized and actionable potential future event that could indicate an upcoming disruption.
Superseder New, superior technology that obviates an existing technology by replacing it.
Surprise Being taken unawares by some unexpected event.1
Techno cluster Geographic concentration of interconnected science- and high-tech-oriented businesses, suppliers, and associated institutions.
Technological innovation Successful execution of a fundamentally new technology or key development in the performance of an existing product or service.
Technology forecasting Prediction of the invention, timing, characteristics, dimensions, performance, or rate of diffusion of a machine, material, technique, or process serving some useful purpose.2
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 Time at which the momentum for change becomes unstoppable (Walsh, 2007).
Trend extrapolation Forecasting method in which data sets are analyzed to identify trends that can provide predictive capability.
TRIZ A forecasting system that uses a set of rules, termed “laws of technological evolution,” that describe how technologies change throughout their lifetimes because of innovation and other factors, resulting in the development of new products, applications, and technologies.
Adapted from the Oxford English Dictionary, available at http://www.askoxford.com/concise_oed/ignorance?view=uk. Last accessed August 25, 2009.
The committee modified the definition of Martino (1969) to reflect the evolving practice of technology forecasting; accordingly, it included the rate of diffusion, which is a critical element in modern forecasting, and defined technology to include materials.