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Persistent Forecasting of Disruptive Technologies – Report 2 PERSISTENT FORECASTING OF DISRUPTIVE TECHNOLOGIES – REPORT 2 Committee on Forecasting Future Disruptive Technologies Division on Engineering and Physical Sciences NATIONAL RESEARCH COUNCIL OF THE NATIONAL ACADEMIES THE NATIONAL ACADEMIES PRESS Washington, D.C. www.nap.edu
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Persistent Forecasting of Disruptive Technologies – Report 2 THE NATIONAL ACADEMIES PRESS 500 Fifth Street, N.W. Washington, DC 20001 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. International Standard Book Number-13: 978-0-309-14904-4 International Standard Book Number-10: 0-309-14904-5 Limited copies are available from Division on Engineering and Physical Sciences National Research Council 500 Fifth Street, N.W. Washington, DC 20001 (202) 334-3118 Additional copies are available from The National Academies Press 500 Fifth Street, N.W. Lockbox 285 Washington, DC 20055 (800) 624-6242 or (202) 334-3313 (in the Washington metropolitan area) http://www.nap.edu Copyright 2010 by the National Academy of Sciences. All rights reserved. Printed in the United States of America
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Persistent Forecasting of Disruptive Technologies – Report 2 THE NATIONAL ACADEMIES Advisers to the Nation on Science, Engineering, and Medicine The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished scholars engaged in scientific and engineering research, dedicated to the furtherance of science and technology and to their use for the general welfare. Upon the authority of the charter granted to it by the Congress in 1863, the Academy has a mandate that requires it to advise the federal government on scientific and technical matters. Dr. Ralph J. Cicerone is president of the National Academy of Sciences. The National Academy of Engineering was established in 1964, under the charter of the National Academy of Sciences, as a parallel organization of outstanding engineers. It is autonomous in its administration and in the selection of its members, sharing with the National Academy of Sciences the responsibility for advising the federal government. The National Academy of Engineering also sponsors engineering programs aimed at meeting national needs, encourages education and research, and recognizes the superior achievements of engineers. Dr. Charles M. Vest is president of the National Academy of Engineering. The Institute of Medicine was established in 1970 by the National Academy of Sciences to secure the services of eminent members of appropriate professions in the examination of policy matters pertaining to the health of the public. The Institute acts under the responsibility given to the National Academy of Sciences by its congressional charter to be an adviser to the federal government and, upon its own initiative, to identify issues of medical care, research, and education. Dr. Harvey V. Fineberg is president of the Institute of Medicine. The National Research Council was organized by the National Academy of Sciences in 1916 to associate the broad community of science and technology with the Academy’s purposes of furthering knowledge and advising the federal government. Functioning in accordance with general policies determined by the Academy, the Council has become the principal operating agency of both the National Academy of Sciences and the National Academy of Engineering in providing services to the government, the public, and the scientific and engineering communities. The Council is administered jointly by both Academies and the Institute of Medicine. Dr. Ralph J. Cicerone and Dr. Charles M. Vest are chair and vice chair, respectively, of the National Research Council. www.national-academies.org
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Persistent Forecasting of Disruptive Technologies – Report 2 COMMITTEE ON FORECASTING FUTURE DISRUPTIVE TECHNOLOGIES GILMAN G. LOUIE, Chair, Alsop Louie Partners, San Francisco PRITHWISH BASU, BBN Technologies, Cambridge, Massachusetts HARRY BLOUNT, DISCERN, Hillsborough, California RUTH A. DAVID, Analytic Services, Inc (ANSER), Arlington, Virginia STEPHEN DREW, Drew Solutions, Inc., Summit, New Jersey MICHELE GELFAND, University of Maryland, College Park DANNY GRAY, Case Western Reserve University, Cleveland, Ohio JENNIE S. HWANG, H-Technologies Group, Cleveland, Ohio ANTHONY K. HYDER, Univerity of Notre Dame, Notre Dame, Indiana FRED LYBRAND, Elmarco, Inc., Chapel Hill, North Carolina PAUL SAFFO, Saffo.com, Burlingame, California PETER SCHWARTZ, Global Business Network, San Francisco NATHAN SIEGEL, Sandia National Laboratories, Albuquerque, New Mexico ALFONSO VELOSA III, Gartner, Inc., Tucson, Arizona NORMAN D. WINARSKY, SRI International, Menlo Park, California Staff 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
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Persistent Forecasting of Disruptive Technologies – Report 2 Preface 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
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Persistent Forecasting of Disruptive Technologies – Report 2 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.
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Persistent Forecasting of Disruptive Technologies – Report 2 Contents SUMMARY 1 1 INTRODUCTION 11 Study Overview, 11 Report Structure, 12 Defining “Disruptive Technologies,” 13 Pitfalls in Forecasting, 13 A Need for Enhanced Forecasting of Disruptive Technologies, 13 Importance of Forecasting to the Department of Defense, 13 Technology and Disruption in the 21st Century, 14 Key Requirements for System Models, 16 Persistence, 17 Openness and Crowdsourcing, 17 Creativity and Tolerance for Failure, 19 Predictions Versus Roadmaps, 19 Framework for Model Building, 20 Insights from the Workshop, 22 Flexibility and Leadership, 22 Narrative Focus, 23 Funding, 23 Risk Management, 23 References, 24 Published, 24 Unpublished, 24 2 MODEL DESIGN OPTIONS FOR FORECASTING SYSTEMS 25 First Forecasting System: Intelligence Cycle Option, 26 The Input of a Question, 26 Processes, 26
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Persistent Forecasting of Disruptive Technologies – Report 2 Desirable Disruptive Group Participants, 30 Hypothesis Evaluation and Testing, 30 Authoring of Potential Future Narratives, 31 Forecasting System Attributes, 31 Forms of the Forecasting System, 32 Second Forecasting System: Roadmapping Option, 34 Generating Ideas, 34 Evaluation Techniques, 37 Communication to Decision Makers, 38 Third Forecasting System: Crowdsourced Option, 38 Inputs, 40 Processes, 40 Outputs, 42 Structure, 42 Resources, 42 Fourth Forecasting System: Storytelling Option, 42 Evaluation of Models and the Activity, 44 References, 47 3 ANALYSIS AND FINAL THOUGHTS 48 Can a Next-Generation Persistent Disruptive Technology Forecasting System Be Built Using Existing Technologies and Methods?, 48 Features of a Next-Generation System, 49 Six Functions of the Version, 49 The Use of Narrative to Initiate Analysis, 50 Using an Open Platform for the System, 53 Characteristics of a Next-Generation Forecasting System, 55 Building Learning into the System, 55 Success Metrics for Participation, 55 Success Metrics for Outputs, 56 Laying a Foundation for Subsequent Steps, 57 Structural Options, 58 Resources, 59 How the System Might Be Implemented, 60 Goals for Version 1.0, 61 Conclusion, 61 References, 61 APPENDIXES A Biographical Sketches of Committee Members 65 B Meetings and Speakers 70 C Workshop Attendees 74 D Transcript of the Workshop 76 E Transcript of the Workshop’s Breakout Sessions 77 F Visualizations of Workshop Discussions 79
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Persistent Forecasting of Disruptive Technologies – Report 2 Acronyms 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
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Persistent Forecasting of Disruptive Technologies – Report 2 Glossary 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. 1 Definition adapted from http://lewisshepherd.wordpress.com/2008/05/25/is-it-even-possible-to-connect-the-dots./. Last accessed January 28, 2010.
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Persistent Forecasting of Disruptive Technologies – Report 2 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. 2 Joseph L. Bower and Clayton M. Christensen. 1995. Disruptive technologies: Catching the wave. Harvard Business Review. January-February. 3 From http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid). Last accessed November 17, 2009.
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Persistent Forecasting of Disruptive Technologies – Report 2 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. 4 For more information, see http://www.extractingdata.com/web%20scraping.htm. Last accessed January 28, 2010.