PERSISTENT FORECASTING OF DISRUPTIVE TECHNOLOGIES

Committee on Forecasting Future Disruptive Technologies

Division on Engineering and Physical Sciences

NATIONAL RESEARCH COUNCIL OF THE NATIONAL ACADEMIES

THE NATIONAL ACADEMIES PRESS

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Committee on Forecasting Future Disruptive Technologies Division on Engineering and Physical Sciences

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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 view of the organizations or agencies that provided support for the project. International Standard Book Number-13: 978-0-309-11660-2 International Standard Book Number-10: 0-309-11660-0 Limited copies are available from Additional copies are available from Division on Engineering and Physical Sciences The National Academies Press National Research Council 500 Fifth Street, N.W. 500 Fifth Street, N.W. Lockbox 285 Washington, DC 20001 Washington, DC 20001 (202) 334-3118 (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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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 Engi - neering 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 com - munity 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 gov - ernment, 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.or g

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COMMITTEE ON FORECASTINg FuTuRE DISRuPTIvE TECHNOLOgIES GILMAN G. LOUIE, Chair, Alsop Louie Partners, San Francisco PRITHWISH BASU, BBN Technologies, Cambridge, Massachusetts HARRY BLOUNT, Blount Ventures, Hillsborough, California RUTH A. DAVID, ANSER, Arlington, Virginia STEPHEN DREW, Drew Solutions, Inc., Summit, New Jersey MICHELE GELFAND, University of Maryland, College Park JENNIE S. HWANG, H-Technologies Group, Cleveland, Ohio ANTHONY K. HYDER, University of 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., Tuscon, Arizona Staff 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 v

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Preface Technological innovations are key causal agents of surprise and disruption. These innovations, and the disrup - tion 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 Engi - neering (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 character - istics 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 contribu - tions 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 vii

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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. viii

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Contents SUMMARY 1 1 NEED FOR PERSISTENT LONG-TERM FORECASTING OF DISRUPTIVE TECHNOLOGIES 8 Rationale for Creating a New Forecasting System, 10 How a Disruptive Technology Differs From an Emerging Technology, 11 Disruptive Versus Emerging Technologies, 11 What Is a Disruptive Technology?, 11 Forecasting Disruptive Technologies, 13 Useful Forecasts, 15 Tools as Signposts, 15 Tipping Points as Signposts, 15 Report Structure, 16 References, 16 Published, 16 Unpublished, 16 2 EXISTING TECHNOLOGY FORECASTING METHODOLOGIES 17 Introduction, 17 Technology Forecasting Defined, 17 History, 17 Defining and Measuring Success in Technology Forecasting, 18 Technology Forecasting Methodologies, 20 Judgmental or Intuitive Methods, 20 Extrapolation and Trend Analysis, 21 Models, 24 Scenarios and Simulations, 27 Other Modern Forecasting Techniques, 28 Time Frame for Technology Forecasts, 30 Conclusion, 31 References, 31 ix

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x CONTENTS 3 THE NATURE OF DISRUPTIVE TECHNOLOGIES 33 The Changing Global Landscape, 33 Effects of the Education of Future Generations, 34 Attributes of Disruptive Technologies, 34 Categorizing Disruptive Technologies, 37 Disrupter, Disrupted, and Survivorship, 37 Life Cycle, 38 Assessing Disruptive Potential, 40 Technology Push and Market Pull, 41 Investment Factors, 42 Cost as a Barrier to Disruption, 43 Regional Needs and Influences, 43 Social Factors, 44 Demographic Factors, 44 Geopolitical and Cultural Influences, 45 Practical Knowledge and Entrepreneurship, 45 Crossover Potential, 45 Conclusion, 46 References, 47 4 REDUCING FORECASTING IGNORANCE AND BIAS 48 Introduction, 48 Ignorance, 48 Closed Ignorance, 49 Open Ignorance, 49 Bias, 51 Age Bias, 52 Mitigating Age Bias, 52 Cultural Bias, 53 Mitigating Cultural Bias, 54 Reducing Linguistic Bias, 54 Conclusion, 55 References, 55 5 IDEAL ATTRIBUTES OF A DISRUPTIVE TECHNOLOGY FORECASTING SYSTEM 57 Tenets of an Ideal Persistent Forecasting System, 57 Persistence, 58 Openness and Breadth, 58 Proactive and Ongoing Bias Mitigation, 61 Robust and Dynamic Structure, 61 Provisions for Historical Comparisons, 61 Ease of Use, 61 Information Collection, 62 Considerations for Data Collection, 62 Key Characteristics of Information Sources, 64 Potential Sources of Information, 65 Cross-Cultural Data Collection, 69 Data Preprocessing, 70 Information Processing, 72 Trends to Track, 73

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xi CONTENTS Enablers, Inhibitors, and Precursors of Disruption, 76 Signal Detection Methods, 77 Exception and Anomaly Processing Tools, 79 Outputs and Analysis, 82 Signal Evaluation and Escalation, 82 Visualization, 82 Postprocessing and System Management Considerations, 87 Review and Reassess, 87 System Management, 88 Resource Allocation and Reporting, 90 References, 90 Published, 90 Unpublished, 91 6 EVALUATING EXISTING PERSISTENT FORECASTING SYSTEMS 92 Introduction, 92 Delta Scan, 92 Strengths and Weaknesses, 93 TechCast, 95 Strengths and Weaknesses, 95 X-2 (Signtific), 98 Strengths and Weaknesses, 101 Evaluation of Forecasting Platforms, 102 References, Unpublished, 104 7 CONCLUSION 105 Benchmarking a Persistent Forecasting System, 105 Steps to Build a Persistent Forecasting System for Disruptive Technologies, 105 Conclusion, 109 APPENDIXES A Biographical Sketches of Committee Members 113 B Meetings and Speakers 117

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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 xiii

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xiv ACRONYMS AND ABBREVIATIONS 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

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Glossary 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 direc - tion (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 char - acteristically 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. xv

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xvi GLOSSARY 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. 1Adapted from the Oxford English Dictionary, available at http://www.askoxford.com/concise_oed/ignorance?view=uk. Last accessed August 25, 2009. 2The 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.