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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium The Connected Science Model for Innovation—The DARPA Role William B. Bonvillian1 Massachusetts Institute of Technology INTRODUCTION—FUNDAMENTALS OF DEFENSE TECHNOLOGY DEVELOPMENT2 The rise of the U.S. innovation system in the second half of the 20th century was profoundly tied to U.S. World War II and Cold War defense science and technology investment.3 However, this late 20th century military technology evolution was only part of a much bigger picture of innovation transformation. Growth economist Carlotta Perez argues that an industrial and therefore societal transformation has occurred roughly every half century, starting with the begin- 1 The author is currently Director of MIT’s Washington Office and an Adjunct Assistant Professor at Georgetown University. The views herein are his own and not necessarily those of his employer. This article was written in 2006 with updates added in May 2008, reflecting developments only through that date. 2 Major portions of this paper appeared in William B. Bonvillian, “Power Play, The DARPA Model and U.S. Energy Policy,” The American Interest II(2):39-48, 2006, and appear here by permission of that journal. 3 Vernon W. Ruttan, Is War Necessary for Economic Growth, Military Procurement and Technology Development, New York: Oxford University Press, 2006. For a review of the growth of R&D in the United States in the period between the two twentieth century world wars, see A. J. Field, “The Most Technologically Progressive Decade of the Century,” American Economic Review September 2003, p. 1406.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium ning of the industrial revolution in Britain in 1770.4 These technology-based innovation cycles flow in long multi-decade waves. Arguably, not only do these waves transform economies and the way we organize societies around them, they transform military power as well; U.S. military leadership has paralleled its technological innovation leadership. Perez found that the U.S. led the last three innovation waves—the information technology revolution represents the latest. Will this leadership continue? At stake is not only economic leadership but U.S. military leadership. In other words, for the U.S. there has been a deep interaction between war and technology—war has greatly influenced technology evolution, and the converse is also true. While this has been the case for centuries, this interaction has been accelerating. Defense technology cannot be discussed as though it is separate and apart from the technology that drives the expansion of the economy—they are both part of the same technology paradigms. Military historian John Chambers has argued that few of the critical weapons that transformed 20th century warfare came from a specific doctrinal need or request of the military;5 Instead, the availability of technology advances has driven doctrine. If technology innovation is a driving force in both U.S. economic progress and military superiority, and these elements have interacted, we need to understand the causal factors behind this innovation. One factor involves critical institutions, which represent the space where research and talent combine, where the meeting between science and technology is best organized. Arguably, there are critical science and technology institutions that can introduce not simply inventions and applications, but significant elements of entire innovations systems. We will focus on aspects of the U.S. innovation system supported by the defense sector—particularly the Defense Advanced Research Projects Agency (DARPA). An Eisenhower creation, DARPA was the primary inheritor of the WWII connected science model embodied in Los Alamos and MIT’s Rad Lab. DARPA came to play a larger role than other U.S. R&D mission agencies in both the Cold War’s defense technology and the private sector economy that interacted with it.6 DARPA will be used as a tool to explore the deep interaction between U.S. military leadership and technology leadership. As we attempt to understand where DARPA came from, we will also ask where it goes next, particularly in IT, as a way of focusing on the continuing strength of the defense innovation system. 4 Carlota Perez, Technological Revolutions and Financial Capital, Edward Elgar, 2002. See also Robert D. Atkinson, The Past and Future of America’s Economy—Long Waves of Innovation That Power Cycles of Growth, Cheltenham, UK: Edward Elgar, 2004. 5 John Chambers, ed., The Oxford Companion to American Military History, Oxford, UK: Oxford University Press, 1999, p. 791. 6 Richard Van Atta, et al., DARPA Technological Accomplishments: An Historical Review of Selected DARPA Projects, Alexandria, VA: Institute for Defense Analysis, 1991; James C. Goodwin, et al., Technology Transition, Arlington, VA: Defense Advanced Research Projects Agency, 1999, accessed at <http://www.darpa.mil/body/pdf/transition.pdf>.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium ROLE OF TECHNOLOGY INNOVATION AND TALENT IN GROWTH Defense and civilian sector innovation in the U.S. are part of one economic system; that system includes not only sharing the same technology paradigms but sharing the societal wealth—economic growth—thrown off by that economic system, which funds both the military and the technology it increasingly depends on for leadership. Therefore, we need to understand the nature of innovation in economic transformation. Keeping in mind the argument that economic growth has dramatically affected military transformation, what are the causal factors in economic growth? To briefly summarize three plus decades of work in growth economics, Professor of Economics Robert Solow of MIT won the Nobel Prize in 1987 because he was profoundly dissatisfied with the growth model of classical economics, where growth was understood in a static model of the interaction between capital supply and labor supply. Solow posited a dynamic model, arguing that while capital and labor supply remained significant, there was a much bigger factor. Studying five decades of U.S. economic growth he found that more than half of this growth flowed from technological and related innovation.7 He argued that growth rates aren’t in an equilibrium but can be altered through innovation advance, with societal well-being expanding correspondingly. The key factor behind his growth through innovation thesis, his work suggests, was the research and development system. However, because technology development is complex and not easy to measure, he treated it as “exogenous” to the economy. Professor of Economics Paul M. Romer of Stamford University articulated what I will call a second direct growth factor.8 If the first is Solow’s technological innovation founded on R&D, Romer argued that knowledge drives economic growth, and that it is an “endogenous” element in the economy. The key factor standing behind this knowledge is science and technological talent, the “human capital engaged in research.” He suggested a prospector theory of innovation–the nation or region that fields the largest number of well-trained prospectors will find the most gold, i.e., the most innovative advances.9 These two direct factors, in shorthand, talent and R&D, don’t stand in isolation from each other, they are interacting parts of an intricate ecosystem of innovation. There are many other factors that are important parts of this system, elements that are more indirect, implicit, and peripheral to innovation advance than the two direct factors essential to economic growth posited above, but these 7 Robert M. Solow, Growth Theory: An Exposition, New York: Oxford University Press, 2nd edition 2000, pp. ix-xxvi (Nobel Prize Lecture, December 8, 1987), accessed at <http://nobelprize.org/nobel_prizes/economics/laureates/1987/solow-lecture.html>. 8 Paul Romer, “Endogenous Technological Change,” Journal of Political Economy 98:72-102, 1990. 9 See discussion of Solow and Romer in David Warsh, Knowledge and the Wealth of Nations, New York: W.W. Norton, 2006.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium indirect factors are nonetheless ones that a society must also get right for innovation advance. The list of indirect innovation factors is long and, because growth economics is relatively new to the economics scene, the metrics for understanding the interaction of these factors are largely unexplored. On the government side they include fiscal, tax, and monetary policy, trade policy, technology standards, technology transfer policies, government procurement, intellectual property protection, the legal and liability systems, regulatory controls, accounting standards, and export controls. On the private sector side, which in a capitalist enterprise must dominate innovation, they include investment capital, including angel, venture, IPO’s, equity, and lending, markets, management principles and organization, talent compensation and reward and quality of plant and equipment. Keep in mind that that these direct and indirect innovation factors all interact and it is the interaction that is most important. Therefore, they represent a common system for both economic and defense sector advance.10 IS THERE A THIRD DIRECT INNOVATION FACTOR? In addition to the two direct and the numerous indirect innovation factors suggested above, arguably there is a third direct factor: the way that R&D and talent, in particular, come together to form an innovation system. In other words, if R&D is factor A, and talent is factor B, they form an interacting combination, AB, which in itself is a third factor, the meeting space for science and technology and the talent behind it. It is not enough to have the ingredients of R&D and talent, they have to come together in an effective way for a highly productive innovation system. We’ll call this third factor innovation organization. Linking two factors 10 We have been discussing innovation in the context of economics, but growth economics, because it is founded on a dynamic model of innovation, has begun to break down the focus of economics, since the late 1940s (neoclassical economics) on the mathematical modeling suited to analysis of limited numbers of variables in a closed equilibrium. Instead, as growth economist Brian Arthur has argued, innovation can create increasing returns not just diminishing returns, leading to transformational phase shifts in an economy. Growth economics requires not only the neo-classical economics of physics-like fundamental principles subject to formulaic proof, but an economics of complexity, where a rich array of interacting elements must be accounted for in systems that are not static but evolve. For example, if innovation organization is a key factor in innovation and therefore economic growth, this element pushes economics towards its original roots in the social sciences and away from neo-classical economic modeling which cannot fully capture organizational elements. This concept puts an orange in what economics has viewed as a mix of apples. In other words, growth economics is gradually broadening economics’ explanatory depth and toolset to reach and understand complex systems, and the third innovation factor discussed below, innovation organization, arguably pushes it further in that direction. See, generally, M. Mitchell Waldrop, Complexity: the Emerging Science at the Edge of Order and Chaos, New York: Simon & Schuster, 1992, pp. 144-148, 250-255, 284-313, 325-327. Since the author drafted this article and footnote in 2006, another book has been published discussing some of these points, Eric D. Beinhocker, Origin of Wealth—Evolution, Complexity, and the Radical Remaking of Economics, Cambridge, MA: Harvard Business School, 2007.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium together, AB, is shorthand in math for multiplying them; arguably, there is a multiplier factor here, too—the way R&D and talent join and are organized can be a multiplier for each. If innovation organization is a kind of multiplier for the two key direct innovation factors, then the way defense and civilian innovation systems organize R&D and talent, and the massive areas where the two systems overlap, will be profoundly determinative of innovation advance for the two systems, and therefore of economic and military leadership. What does innovation organization look like? This factor must be seen and understood at least two levels, the institutional level and the personal, face-to-face level. We will explore these in succession. U.S. INNOVATION ORGANIZATION AT THE INSTITUTIONAL LEVEL Governmental science and technology organization in the U.S. largely dates from WWII and the immediate post-war. As suggested earlier, technology evolution in this country comes from a kind of “PushMi-Pullyu” relationship between civilian economic and defense sectors, and WWII was a transformative period where the pressure for military technology advance later led to a dramatic economy-wide advance. Vannevar Bush led this charge,11 acting as President Roosevelt’s personal science executive during the war. He was allied to a remarkable group of fellow science organizers, including Alfred Loomis, an investment banker and scientist, physicist Ernest Lawrence of Berkley, and two university presidents, James Conant of Harvard and Arthur Compton of MIT. Successively, Bush created and took charge of the two leading organizing entities for U.S. science and technology, the National Defense Research Council (NDRC) and then the Office of Science Research and Development (OSRD). These became the coordinating entities for U.S. wartime R&D, creating crash research projects in critical areas, such as the Rad Lab at MIT and Los Alamos, and the and, in turn, insured interaction and coordination with a rich mix of research components. Influenced by the frustrations of his WWI military research experience where technology breakthrough could not transition past bureaucratic barriers into defense products, Bush kept civilian science control of critical elements of defense research, insisting that his science teams stay out of uniform and separate from military bureaucratic hierarchies which he found unsuited to the close-knit interaction needed for technology progress. 11 G. Pascal Zachary, Endless Frontier: Vannevar Bush, Engineer of the American Century, Cambridge, MA: The MIT Press, 1999. See also Jennet Conant, Tuxedo Park, New York: Simon & Shuster, 2002 (biography of Alfred Loomis, founder of MIT’s Rad Lab). For a discussion of U.S. pre-WWII science organization see David Hart, Forged Consensus, Princeton, NJ: Princeton University Press, 1998.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium To summarize, Bush brought all defense research efforts under one loose coordinating tent, NDRC then OSRD, and set up flat, non-bureaucratic, interdisciplinary project teams oriented to major technology challenges, like radar and atomic weapons, as implementing task forces. He created “connected” science, where technology breakthroughs at the fundamental science stage were closely connected to the follow-on applied stages of development, prototyping and production, operating under what we will call a technological “challenge” model. Because Bush (and his ally Loomis) could go directly to the top for backing from Roosevelt, through Secretary of War Henry Stimson and Presidential Aide Harry Hopkins, Bush made his organizational model stick during the war, despite relentless military pressure, from the Navy in particular, to capture it. Then, immediately after the war, he systematically dismantled his remarkable connected science creation. Envisioning a period of world peace, convinced that the wartime levels of government science investment would be slashed, and probably wary of a permanent alliance between the military and science, Bush decided to try and salvage some residual level of federal science investment. He wrote the most influential polemic in U.S. science history, “The Endless Frontier,” for Roosevelt, arguing that the federal government should fund basic research, which would deliver ongoing progress in economic well-being, national security and health to the country.12 In other words, he proposed ending his model of connected science, and dropping his challenge model, in favor of making the federal role one of funding one stage of technology advance, exploratory basic research. His approach would become known as the “pipeline” model for science investment. The federal government would dump basic science into one end of an innovation pipeline, and somehow early and late state technology development and prototyping would occur inside the pipeline, with new technology products emerging, genie-like, at the end. Because he assembled a connected science model during WWII, Bush no doubt realized the deep connection problems in inherent this pipeline model, but likely felt that salvaging federal basic research investment was the best he could achieve in a period of anticipated peace. He did argue that this basic research approach should be organized and coordinated under “one tent” to direct all the nation’s research portfolios, proposing what would become the National Science Foundation (NSF). Because he wanted this entity controlled by a scientific elite separated from the nation’s political leadership, Bush got into a battle with Roosevelt’s successor, Harry Truman. In his typical feisty, take-charge way, Truman insisted that the scientific buck would stop on his desk not on some Brahmin scientist’s desk, and that NSF appointments would be controlled by the President. Bush disagreed. 12 Vannevar Bush, Science: The Endless Frontier, Washington, D.C.: U.S. Government Printing Office, 1945, p. 1-11) (FDR and Bush letters, Summary, Introduction). Available at <http://www.nsf.gov/od/lpa/nsf50/vbush1945.htm>.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium Truman therefore vetoed Bush’s NSF legislation, stalling its creation for another five years.13 Meanwhile, science did not stand still. New agencies proliferated, and the outbreak of the Korean War led to a renewal of defense science efforts. By the time NSF was established and funded, its potential coordinating role had been bypassed. It also became a much smaller agency than Bush anticipated, only one among many. Despite Bush’s support for one tent where scientific disciplines and agencies could coordinate their work, as they did in WWII, the U.S. thus adopted a highly decentralized model for its science endeavor.14 Bush’s concept of federal funding focused on basic science did prevail, however, with most of the new science agencies adopting this model for the federal science role. These twin developments left U.S. science fragmented at the institutional level in two ways: overall science organization would be fragmented among numerous science agencies, and federal investment would be focused on only on one stage of technological development, exploratory basic research.15 Remarkably, Bush left a legacy of two conflicting models for scientific organizational advance: the connected, challenge model of his WWII institutions, which he dismantled after the war,16 and the fundamental-science focused, disconnected, multi-headed model of post-war U.S. science institutional organization. 13 William A. Blanpied, “Inventing U.S. Science Policy,” Physics Today 51(2):34-40, 1998 (post-WWII evolution of U.S. science organization and NSF); George Mazuzan, The National Science Foundation: A Brief History (1950-1985), (NSF 88-16), Arlington, VA: The National Science Foundation, 1988. Available at <http://www.nsf.gov/pubs/stis1994/nsf8816/nsf8816.txt>, pp. 1-25 (history of NSF in the context of post-WWII science). 14 It must be emphasized that there are major advantages to decentralized science. It creates a variety of pathways to science advance and a series of safety nets to ensure multiple routes can be explored. Since science success is largely unpredictable, the “science czar” approach risks major failures that a broad front of advance does not. Nonetheless, the U.S. largely lacks the ability to coordinate its science efforts across agencies particularly where advances that cut across disciplines require coordination and learning from networks. 15 See discussion of these developments in, Donald E. Stokes, Pasteur’s Quadrant: Basic Science and Technological Innovation, Washington, D.C.: Brookings Institution Press, 1997. 16 The term “dismantled” is used to indicate that the structure for science management in WWII was ended, and many wartime science entities were shut down, including MIT’s Rad Lab. Obviously, other existing science entities continued in operation, such as NACA, which Bush chaired before the war, and was an early example of a connected, challenge model approach. See Alex Roland, Model Research: The National Advisory Committee for Aeronautics, 1915-1958, pp. 225-258 (Ch. 10), Washington, D.C.: National Aeronautics and Space Administration, available at <http://history.nasa.gov/SP-4103/>. However, even within DoD, the Office of Naval Research was largely set up after the war around a fundamental science model. Harvey M. Sapolsky, Science and the Navy—The History of the Office of Naval Research, Princeton, NJ: Princeton University Press, 1990, pp. 9-81 (Ch. 2-4).
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium SUMMARY OF THE INNOVATION ANALYTICAL FRAMEWORK To summarize the discussion thus far, innovation is not only about R&D investment levels, it’s about content and efficiency.17 U.S. post-war policy institutionally severed R from D, which had been connected in the wartime model, and posited a pipeline theory of innovation where the federal government dumped research funding into one end of the pipeline, then mysterious things occurred within the innovation pipeline, then remarkable products emerged at the other end. Neoclassical economics, through the work of Robert Solow, came to realize the central role of innovation in economic growth but was unable to apply existing economic models to the mystery inside the pipeline, so treated innovation as “exogenous” to the economy. That response was ultimately unacceptable—it as though economics, after finally discovering the innovation monster in the economic growth room, then declined to look at it. So a group of growth economists, initially led by Paul Romer, gradually began to whittle away at the monster, treating it as “endogenous,” slowly delineating its economic attributes. However, this delineation process still has barely begun.18 Economic institutions still collect extensive data on the two factors classical economics tied to economic growth, capital supply and labor supply, and data on R&D investment totals; we have little data on the monster, the content and efficiency of the innovation system.19 Few are searching for and analyzing the new factors and metrics for innovation evaluation. Interestingly, two decades after Solow won the Nobel Prize for identifying the innovation monster, the U.S. Department of Commerce has announced the need to begin an intensive data collection process around innovation, although this effort is not yet funded.20 The National Science Foundation, which has long collected data on innovation investment levels and science education,21 has begun an effort to look at data and analysis around innovation with a program entitled the Science of Science and Innovation Policy. But what is the framework for the innovation metrics and analysis? Although we track R&D investment, what about the composition and efficiency factors? This paper attempts to identify some of the elements lurking inside the innovation 17 Gregory Tassey, The Innovation Imperative, Cheltenham, UK: Edward Elgar, 2007, Ch. 3, 7, 8. 18 For a critical view of the progress of endogenous growth theory in economics, see Robert Solow, “Toward a Macroeconomics of the Medium Run,” Journal of Economic Perspectives Winter 2000. 19 Despite the emergence over two decades ago of growth economics and its doctrine that growth is predominantly innovation based, the two U.S. political parties are still largely organized around the old factors posited by classical economics as responsible for growth, capital supply and labor supply. 20 U.S. Department of Commerce, Innovation Measurement: Tracking the State of Innovation in the American Economy, Report to the Sec. (Jan. 2008), Washington, D.C.: U.S. Department of Commerce, available at <http://www.innovationmetrics.gov/Innovation%20Measurement%2001-08.pdf>; Michael Mandel, “A Better Way to Track the Economy: A Groundbreaking Commerce Dept. Report Could Lead to New Yardsticks for Measuring Growth,” Business Week Jan. 28, 2008, p. 29. 21 National Science Board, Science and Engineering Indicators, Arlington, VA: National Science Foundation, 2006, available at <http://www.nsf.gov/statistics/seind06/toc.htm>.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium pipeline. Following Solow and Romer, it argues, as noted, that R&D and talent (shorthand terms for their extended ideas) can be considered two direct innovation factors, indispensable to innovation, and are surrounded by an ecosystem of indirect factors, less critical but none the less significant. This paper further posits that there is a third direct innovation factor, innovation organization, the space where the talent and R&D converge. An essential aspect of innovation organization requires evaluation at the institutional level. Summarized above is the brilliant success the U.S. experienced at the institutional level during WWII with a connected science model built around technological challenges, formed under one organizational tent. The U.S., following the war, shifted to a highly-decentralized model, scattering government-funded research among a series of mission agencies. It was predominantly a basic-science focused model, not connected science, and left what later became known as a “valley of death” between research and development stages, so the handoff from publicly-funded research and to private sector development lacked institutional bridging mechanisms. As we will see, the major exception to that U.S. institutional rule was DARPA.22 We turn now from a review of innovation at the institutional level to a second analytical perspective on innovation organization, innovation at the personal, face-to-face level. Following this review, we will examine how these twin perspectives on innovation organization have operated within an arguably critical U.S. innovation organization, DARPA, evaluating how it has worked at both levels, institutional and personal. INNOVATION SYSTEMS AT THE PERSONAL LEVEL—GREAT GROUPS Innovation organization should be analyzed it the institutional level, as discussed above, but also requires understanding at the ground level, from the personal, face-to-face point of view. Innovation is different than scientific discovery or invention, which can involve solo operators. Instead, innovation requires taking both scientific discovery and invention and piling applications on a breakthrough 22 This is not to assert that the fundamental science mission agencies dating from the 1940s have remained frozen in time. While the basic science mission remains paramount at agencies such as NSF, NIH and the DoE Office of Science, at the National Science Foundation, for example, there is funding not only for small individual investigator basic research but larger areas of interdisciplinary advance, such as nanotechnology, which can incorporate grand challenges. For example, NSF’s issue workshops and similar organizing mechanisms bring in ideas for coordinated science-engineering advance for initial buy-in and research program design by fundamental and applied communities. As another example, NSF’s engineering directorate supports engineering centers tying science advance to fundamental engineering advance. Somewhat similar efforts around interdisciplinary centers have evolved at NIH and DoE. The point remains that these functions supplement established fundamental science efforts.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium invention or group of inventions to create disruptive productivity gains that transform significant segments of an economy and/or defense system. So innovation is a third phase built on phases of discovery and invention. Innovation requires not only a process of creating connected science at the institutional level, it also must operate at the personal level. People are innovators, not simply the overall institutions where talent and R&D come together. Warren Bennis and Patricia Beiderman have argued that innovation, because it is much more complex than the earlier stages of discovery and invention, requires “great groups” not simply individuals.23 Rycroft and Kash make a similar argument but use a different term: Innovation requires collaborative networks24 which can be less face-to-face and more virtual. As we look at innovation organization at the personal level, we will explore the rule sets for three sample “great groups” of innovators. 1. Edison’s “Invention Factory” at Menlo Park, New Jersey Thomas Edison formed the prototype for innovator great groups.25 Edison placed his famous Menlo Park lab in a simple 100-foot long wooden frame building, a lab, on his New Jersey farm. In it, he placed a team of a dozen or so artisans, mixing a wide range of skills with a few trained scientists. They worked intensely, sometimes 24/7, and took midnight breaks together, eating pies, reciting poems and singing songs. They mixed a range of disciplines and organize their intense effort around the challenge of electric light. They were a great group, highly collaborative. Great groups also require collaboration leaders and Edison was a remarkable team leader. They worked on the idea of filling the gap between electric poles with a filament placed in a vacuum tube. But that was only the breakthrough invention, not the innovation. To make their light usable, Edison and his team then must invent much of the infrastructure for electricity—from generators to wiring to fire safety to the structure of a supporting electric utility industry. Edison and his team become inventors and innovators, visionaries and (as initiators of a network of companies with Wall Street backing) vision enablers. Interestingly, as part of this process, Edison had to derive elements of electron theory to explain his results—his “Edison Effect” helped lead to atomic physics advances. There is a major lesson in this: Science is not simply a linear pipeline going from basic to applied, it goes both ways: basic to applied and applied to basic. Menlo Park teaches us parts of the rule set for great groups. It is organized around a challenge model, with the group trying to solve a specific challenge or goal; it applies an interdisciplinary mix of both practical and basic science to get 23 Warren Bennis and Patricia Ward Biederman, Organizing Genius, New York: Basic Books, 1997. 24 Robert W. Rycroft and Don E. Kash, “Innovation Policy for Complex Technologies,” Issues in Science and Technology, Fall 1999. 25 See discussion in Sir Harold Evans, They Made America, Sloan Foundation Project, New York: Little, Brown and Company, 2005, pp. 152-171.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium there, and it uses a connected science model, tying invention to innovation and incorporating all stages of innovation advance. The group was under Edison’s clear leadership, and that leadership factor was vital, but it was a non-hierarchical, relatively flat, two-level, highly collaborative effort. The team mixed experimentalists and theorists, artisans and trained scientists and engineers, for a blend of experimental and theoretical capability and disciplines. 2. Alfred Loomis and the Rad Lab at MIT, 1940-1945 Alfred Loomis loved science but family needs compelled him to become lawyer; he combined his science and legal skills to become a leading Wall Street financier for the emerging electric utility industry in the 1920s.26 Anticipating the market crash, he sold out in 1928 with his great fortune intact. He used it to pursue science, setting up his own private lab at his Tuxedo Park, New York estate in the 1930s and assembling there a who’s who of pre-war physics. Loomis’ own field of study there was microwave physics. As WWII loomed, Vannevar Bush, respecting Loomis’ industrial organizing skills, asked him to join Roosevelt’s NRDC to mobilize science for the war. Because the American military was initially uninterested, the British handed over to Loomis a suitcase with their secrets to microwave radar in his penthouse in the Shoreham Hotel in Washington in 1940. As the Battle of Britain raged, Loomis’ microwave expertise enabled him to grasp immediately that this was a war winning technology for air warfare. He promptly persuaded his cousin and mentor, Secretary of War Henry Stimson, that this technology must be developed and exploited without delay. With Bush’s and Roosevelt’s immediate approval, Loomis within two weeks set up the Radiation Laboratory (Rad Lab) at MIT. Because he knew them from his Tuxedo Park lab, Loomis and his ally and friend Ernest Lawrence of Berkeley called in the whole talent base of U.S. physics to join the Rad Lab, and nearly all came. Because the government was not used to establishing major labs literally overnight, Loomis personally funded the startup while government approvals and procurement caught up. The Rad Lab was non-hierarchical and flat, with only two levels, project managers and project teams, each devoted to a particular technology path. It was characterized by intense work, often around the clock, and by high spirits and morale. Loomis and Bush purposely kept it out of the military. The Rad Lab used a talent base with a mix of science disciplines and technology skills, it was highly collaborative, it was organized around the challenge model, and it used connected science, moving from fundamental breakthrough to development, prototyping and initial production. Interestingly, the Rad Lab organizational model was systematically adopted at Los Alamos, and ten leading Rad Lab scientists shifted to Los 26 Details from Loomis’ biography, Jennet Conant, Tuxedo Park, op. cit.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium has been that semiconductor advance should be led by industry, increasingly dominated in the U.S. by mature, large-scale firms that DARPA’s leaders feel should manage their own problems. But if industry increasingly is being forced to shift abroad because of cost pressure from massive industrial subsidies available there,50 DoD has a long term problem with what still appears to be a foundation technology. It is serious enough that a 2005 Defense authorization bill directed DoD to implement DSB’s proposals to try to control the problem and retain U.S. technology leadership in this area.51 A DARPA chip strategy, some would argue, should be to try to secure leadership in a post-silicon, post-Moore’s Law world in bio-nano-quantum-molecular computing; DARPA would respond that it is working in a number of those fields. Others would dispute whether it is doing enough to nurture leadership in these emerging areas. STATUS OF THE HYBRID MODEL More broadly, DSB notes that one of DARPA’s critical roles was to fund through its applied research portfolio (known in DoD as “6.2”) “hybridized” university and industry efforts through a process that envisioned revolutionary new capabilities, identified barriers to their realization, focused the best minds in the field on new approaches to overcome those barriers and fostered rapid commercialization and DoD adoption.” The hybrid approach bridged the gaps between academic research and industry development, keeping each side knowledgeable about DoD’s needs, with each acting a practical prod to spur on the other. DSB expressed concern that this fundamental DARPA approach was breaking down as it cut back its 6.2 university computer science investments, and shifted more of its portfolio to classified “black” research, under pressure from the ongoing war, which cannot include most universities and non-defense tech firms, and, so DSB suggested, reduces DARPA’s intellectual mindshare on critical technology issues.52 50 Thomas Howell, “Competing Programs: Government Support for Microelectronics,” in National Research Council, Securing the Future: Regional and National Programs to Support the Semiconductor Industry, Charles W. Wessner, ed., Washington, D.C.: The National Academies Press, 2003; Thomas Howell, et al., China’s Emerging Semiconductor Industry, San Jose, CA: Semiconductor Industry Association, October 2003. 51 Defense Auth. Act for 2005, H.R. 1815 (Sen. Amend. 1361). DoD has established a “trusted foundry” program, initiated in cooperation with IBM, to try to protect its own access to a stable supply of secure semiconductor chips, a particular concern of intelligence agencies, but this does not assure it long term access to technology leadership in what many continue to argue remains a critical technology. 52 Total DARPA university funding as a percentage of DARPA science and technology funding fell from 23.7 percent in FY2000 to 14.6 percent in FY2004 according to 2005 DARPA data, supplied with hearing testimony, op. cit. A series of major university computer science research department underwent DARPA funding cutbacks of 50 percent and more in the past six years; some observers have argued that new generations of graduate students are no longer trained in DARPA-hard problems and tied to the agency, so that DARPA has reduced connections to its future talent base.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium GRID SECURITY PITAC’s report53 on cybersecurity noted DARPA plans to terminate funding for its High Confidence Software and Systems development area, aiming to curtail cybersecurity funding except for classified work. Historically, one of Eisenhower’s key aims in establishing DARPA was to make sure the U.S. was never again subject to a major technology surprise like Sputnik, and it is widely acknowledged that defense and critical private sector IT systems remain vulnerable to cybersecurity attack. Defense theorists, noting the major economic consequences of the 9/11 attack on financial markets and the insurance sector have argued that asymmetric cyber attacks on fundamental financial infrastructure by largely unidentifiable state or non-state actors could be devastating to the developed world, potentially striking a powerful blow to the world economy. PITAC has noted that because IT is dominated by the private sector, and even DoD’s proposed secure high speed Global Information Grid must interact with the internet, shared solutions between defense and private sectors must be developed, so classified research in many cases cannot be effectively implemented. PITAC identified ten defense-critical IT research areas, from authentication technologies to holistic security systems, it believes require future DARPA investment. ALTERING THE ECOSYSTEM Dr. Thomas Leighton, Chief Scientist of Akamai Corp., in response to questions from the House Science Committee, argued that DARPA’s most important contribution to IT has been, “its unique approach (and commitment) to developing communities of researchers in both industry and academia” focused on “‘pushing the envelop’ of computer science.”54 Although DARPA continues to look at some IT problems, “its growing failure to support the university elements of that community is altering the innovation ecosystem” that it created “in an increasing negative way, with no other agency ready or able to pick up that role.” Some university computer science departments and labs report that although the DARPA cutbacks in funding have been at least partially made up by industry support, this is often short term and not breakthrough-oriented, and often is from Asian firms that control the IP for technology developed and for obvious competitive reasons preclude it going into U.S. spin-offs. It should be noted that an increase in NSF computer science funding has offset some of the effects of the decline in DARPA university funding. DARPA’s leadership has argued, as justification for the cutback, that it was not seeing enough new ideas from this sector. 53 President’s Information Technology Advisory Committee, Report to the President, “Cybersecurity: A Crisis of Prioritization,” op. cit. 54 Response of Dr. Tom Leighton to Questions from the House Science Committee, July 7, 2005, op. cit.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium Dr. William Wulf, a computer scientist and until recently President of the National Academy of Engineering, told the House Science Committee that, “There is now no DoD organization like the ‘old DARPA’ … that fills the role of discovery of breakthrough technologies.”55 Although he acknowledged that DARPA was looking at cognitive computing, he argued that there were problems in the subjects DARPA was selecting for IT research because it was not confronting key security areas. For example, “our basic model of computer security (perimeter security) is fatally flawed” and will not be solved by the “short term, risk-adverse approach being currently taken by DARPA.” He argued that our “ability to produce reliable, effective software” is tottering on “the brink of disaster” but DARPA has not focused on solutions, and also is not reviewing the fact that our basic model for computing is not yet close to human brain capability, and requires a new model “of parallel computing” with “architectures and algorithms of immense power.” He also argued that the “use of computers in education has progressed little from the ‘automated drill’ model of the Plato system of the 1960s” although “we know much more about how people learn physiologically and psychologically” including how “emotion interacts with learning” which we could put to good use in quickly training troops in urban combat and counter-insurgency, and DARPA should also be more involved in this area. DARPA spokesmen have noted in response to these arguments that DARPA has funded, as has the Army, soldier training simulation systems at USC’s center for this work, and that it was the primary initial funder of grid computing. Perhaps one part of the answer is that DARPA may lack a Licklider with the vision to see and evolve a new IT territory. Critics respond that that because of a top-down management style in recent years at DARPA, office directors and program managers lack the authority to initiate in this way. It is generally understood that DARPA has had to be increasingly focused on solving a problem it ran into at the end of the Cold War with its resulting cuts in defense procurement starting in 1986: the breakdown of technology transition from DARPA into services. DARPA even during the Cold War had a transition problem with the services as it focused on disruptive, change-state, radical innovation. It solved some of these problems in the past by transitioning technology, such as IT, into the civilian economy, In other areas, it had to rely on the clout of the Secretary of Defense and, when available, a strong Director of Defense Research &Engineering (DDR&E). DARPA typically did not enjoy a consensus with the military unless it was hammered out by the Office of the Secretary of Defense and the service secretaries. Nonetheless, following the Cold War, technology transition declined. Unsuccessful in building a new consensus with the military services for transferring the results of revolutionary technology investment into service procurement, DARPA technology strategy has been moving from its his- 55 Dr. William A. Wulf, Response to Questions from the House Science Committee, July 2005, op. cit.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium tory of radical innovation to more incremental innovation, shifting a larger part of its investment into later stage development efforts that the services are more ready to invest in. Defense budget analysts report that shorter term incremental work, space launch, and satellite “repair” are requiring growing parts of the DARPA budget. A new DARPA review process, mandated by improving transition to the services, of frequent “up or out” decisions with limited development time is placing more of its R&D on a shorter-term course. Congress may be playing a role in this, as well, focusing more on DARPA’s record rather than it’s overall impact. The current emphasis on a pre-agreed transition plan may further limit disruptive work. Some believe that resulting more frequent policy reversals and turns may limit DARPA’s ability to mount enough creative, longer-term investment programs so important to past development. Although the heart of DARPA’s creativity in the past was in highly talented and empowered project managers, some believe that the role of project managers has been significantly limited by this short term review approach. Although DARPA has always been able to pick among the brightest technologists in the nation, its larger focus on classified programs56 may limit its access to some of the university researchers it has relied on in the past, creating difficulty over time in attracting talent. DARPA in the past has operated in both the civilian and defense economies, understanding they are the same economy. As noted, it has built “great groups” and spun off civilian-relevant technology, such as in computing, to the civilian sector where it evolved further, enabling DoD to buy it back at radically lower costs and to take advantage of civilian development advances. Alternatively, it has spun off to the defense sector defense-only technologies like stealth and unmanned aerial vehicles (UAV’s). DARPA’s need to focus on the current asymmetric conflict and corresponding classified work, as well as shorter term technology transition, may make it less able to spin off technology to the civilian economy, despite DoD’s growing capital plant cost crisis and its need to take better advantage of advances in that sector.57 Given DARPA’s historic role in successfully straddling both sectors, DARPA’s needs to protect its ability to play in both worlds. Much of the above debate is driven by IT sector concerns. But there is a larger debate emerging over DARPA’s role in IT. Because DARPA, starting with Licklider, played a profound role at the center of most aspects of the IT revolution, there is a question whether its current focus on shorter-term and classified 56 DARPA has always had, of course, a large classified program base separate from its academic research. The assertion here is that the balance has changed with more of a tilt toward classified work. 57 Research investment also affects defense capability. With defense R&D, nations generally “get what they pay for,” with weapon system capability and quality directly corresponding to intensity of research investment. Andrew Middleton and Steven Bowns, with Keith Hartley and James Reid, The Effect of Defense R&D on Military Equipment Quality, Defense and Peace Economics 17(2):117-139, April 2006.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium programs due to the war inevitably will signal a broader retreat from this sector58 and does the state of the sector justify such a retreat?59 The first question that must be asked is where are we in the IT revolution? In the past, innovation waves fully matured in 40 or 50 years and society moved on to the next innovation stage. Accordingly, some argue that the IT revolution is maturing and that we need to move on to the next big things.60 Where do we measure the IT wave from? If we measure it from the first post-World War II mainframe, ENIAC, the half century mark for the revolution ran out in 1995. 1995, however, was the period when we were bringing on personal computing and internet access at levels that reached a major portion of our society. If we measure the IT innovation wave from around 1995, when real time and networked computing took off with the public, then we are still a decade into an IT revolution wave. Perhaps DARPA should be moving on to another innovation wave? On the other hand, the IT revolution may be different from steam engines or electricity. The four- or five-decade model for past innovation waves may not be fully relevant to the IT revolution. When we work with the information domain, we have to keep in mind that we are working with a fundamental force that Norbert Weiner suggested in 1948 was a coequal to mass and energy.61 We have already been through a succession of unfolding and sometimes parallel IT waves, from business (and military) computational capability, to data retrieval, processing and display, to advanced digital communications, to data mining and using mass data as a predictive tool, and we are beginning to make progress on symbolic manipulation and computer theorem proving and are thinking about quantum computing. The grail quest of computing is true artificial intelligence. This is not a technology pursuit similar to past efforts because it is ultimately a quest to take on a godlike power.62 We have a long, long way to go in achieving this stage. Progress on the Turing Test—can a computer’s thinking be mistaken for a human’s—has been limited.63 Although computers now play chess at the highest level and drive SUVs through DARPA’s desert and urban obstacle courses, computing isn’t even close yet to the intuitive powers of the human brain. Although an artificial intelligence quest may ultimately be futile or only partially achievable, even if we have to 58 Vernon Ruttan has raised the concern that with the post-Cold War decline in defense innovation, the U.S. innovation system may not now be strong enough to launch new breakthrough technologies in either the public or the private sector. Vernon W. Ruttan, “Will Government Programs Spur the Next Breakthrough?” Issues in Science and Technology Winter 2006. 59 House Science Committee Committee Hearing on the Future of Computer Science Research in the U.S. (Testimony of William Wulf and Thomas Leighton) and letters from same in response to Committee questions, op. cit. 60 Robert Atkinson, “Is the Next Economy Taking Shape?” Issues in Science and Technology Winter 2006, p. 62. 61 Norbert Wiener, Cybernetics or Control and Communication in the Animal and the Machine, Cambridge, MA: The MIT Press, 1948. 62 Ann Foerst, God in the Machine, New York: Penguin Books, 2005. 63 Mark Halpern, “The Trouble with the Turing Test,” The New Atlantis 11(Winter 2006):42-63.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium settle for Licklider’s “Man-Machine Symbiosis” we have a long way to go before this more limited vision is close to being played out. In other words, there may be decades of radical, breakthrough innovation to go in IT, not simply incremental advances. If this is right then DARPA, given its historic breakthrough technology mission and responsibility to avoid Sputnik-like technology surprises, continues to have a future in IT. Even setting aside the ultimate artificial intelligence challenge, Victor Zue has argued that the next generation of computing challenges are more profound than ever.64 While yesterday’s problem was computation of static functions in a static environment within well-understood specification, today, adaptive systems are needed that operate in environments that are dynamic and uncertain. While computation was the main past goal, communication, sensing and control are also now critical. While computing used to focus on the single operating agent, it must now focus on multiple agents that may be cooperative, neutral or adversarial. While batch processing of text and homogeneous data used to be the task, stream processing of massive heterogeneous data now is. While stand-alone applications once prevailed, deep interaction with humans is now key. While there was a binary notion of correctness in computing, now there is a trade-off between multiple criteria. In today’s computing world these opportunities arise in a far more complex environment of cheap communication, ubiquitous communication, overwhelming data, and limited human resources. Major IT tasks for the military become, for example, much deeper human computer interface, social and cultural modeling; far more robust and secure computation; smart, self-directed autonomous surveillance; and robots ready for human interaction. DARPA strongly maintains it is funding IT, even though an increasing amount of its work must be classified. It is also funding what it believes is a critical breakthrough area in computing, cognitive computing, and supports bio computing and robotics. The ongoing wars in Iraq and Afghanistan appropriately force DARPA toward shorter term solutions for the military; it went through a similar evolution during the Vietnam War. DARPA has had, as noted, a profound problem with technology transition with the military services and to solve it, must focus on better meeting service needs. Still, the question must be asked whether there is a danger that DARPA may be over time retreating into Iansati’s and Levien’s “landlordism”—not continuously renewing but living off incremental improvements on past advances. For example, it is felt by some observers that DARPA should evaluate its tactical technology vision as that program threatens to become increasingly smaller-scale, less coherent and non-tactical. DARPA should also evaluate the emerging new dimensions of whether it has a coherent IT vision for approaching some of the challenges Zue and others suggest. Given DARPA’s unique historical role in U.S. technology advance,65 this is a significant 64 Victor Zue, “Introduction to CSAIL,” MIT, April 15, 2008, pp. 6, 14. 65 Richard H. Van Atta, et al., DARPA Technical Accomplishments, Volumes I-V, op. cit.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium issue. Because even great technology advances take a decade or two to produce, the pipeline of advance is hard to see, but problems we may have now in filling that pipeline will have a profound effect on our future a decade or more out. DARPA is not the only aspect of DoD technology leadership facing difficulties. DoD depends on a strong fundamental physical science research to support its breakthrough potential, but these programs and funding levels are in decline.66 Boomer generation scientists have been the mainstay of DoD science talent in its labs and research centers, but are now retiring in droves, and are not being adequately replaced. DoD faces a very serious science talent supply problem and needs hiring and retention flexibility beyond civil service limits, but a rigid position in the past by DoD personnel staff that there must be only one personnel system for all at DoD has thwarted Congressional reform efforts to create more flexibility for scientists. The pressure of the tempo of ongoing military operations is, in turn, putting pressure on funding for science in the military services. The pattern of technology leadership in DoD may not be as strong as in the past. DDR&E leaders of the caliber of John Foster, Malcolm Currie and William Perry have been infrequent, and the overall depth of technical competence in the Office of the Secretary of Defense to backup DARPA and push for technology implementation has declined. Overall, the picture for DoD science is not getting prettier, and this is against a backdrop of serious problems in U.S. physical science in general, as explored in recent major reports by the National Academies.67 Yet our security challenges are growing. The emergence of the terrorist model, of non-state actors relatively immune to state-to-state pressure, represents a profound asymmetric challenge to a Western military model that has been world-dominant since the 15th century. In parallel is the emergence of other peer competitors, working on both symmetric and asymmetric approaches, pursuing a technology innovation model for economic development which, as discussed, has significant military implications. This raises a fundamental concern: Can U.S. technological superiority be the continuing basis of U.S. security in an increasingly globalized technological and economic world? Since U.S. economic and military success, as argued at the outset, has relied on profound integration between defense and civilian elements of its innovation system for technological superiority both military and economic, consequences on one side of this equation, such as long term DARPA capability, have major effects on the other side. 66 James A. Lewis, Waiting for Sputnik, Washington, D.C.: Center for Strategic and International Studies, March 1, 2006. See also John Young, Director of Defense Research and Engineering, Info Memo for Secretary of Defense Robert M. Gates, DoD Science and Technology Program, August 24, 2007 (need and corresponding proposal for increased DoD S&T funding, listing potential high pay-off research areas). 67 National Academy of Sciences/National Academy of Engineering/Institute of Medicine, Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future, Washington, D.C.: The National Academies Press, 2007; Norman Augustine, Is America Falling Off the Flat Earth? op. cit.
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21st Century Innovation Systems for Japan and the United States: Lessons from a Decade of Change - Report of a Symposium SUMMARY Arguably innovation organization—the way in which the direct innovation factors of R&D and talent come together, how R&D and talent are joined in an innovation system—is a third direct innovation factor. DARPA emerged as a unique model—operating at both the institutional and personal level of science organization. Building on the Rad Lab example, it built a deeply collaborative, flat, close-knit, talented, participatory, flexible system, oriented to breakthrough radical innovation. It has used a challenge model for R&D, moving from fundamental, back and forth with applied, creating connected science linking research, development, and prototyping, with access to initial production. In other words, it followed an innovation path not simply a discovery or invention path. Like all human institutions, these organizational models are transitory. The DARPA model has been one of the longest lasting, unique in the federal government, and seemed to be the most capable of ongoing renewal. But that DARPA model now may be shifting under pressure of ongoing operations, particularly regarding DARPA’s role in the IT sector, with potential long term effects on U.S. defense as well as civilian sector technology superiority. This shift occurs against a backdrop of overall problems in U.S. physical science strength. DARPA has long served a keystone function in the U.S. innovation system and it is in the nation’s national security and economic interest that it continue to avoid “landlord” status. REFERENCES Alberts, David, John Garska, and Frederick Stein. 1999. Network Centric Warfare. Washington, D.C.: Department of Defense Command and Control Research Program. Available at <http://www.dodccrp.org/files/Alberts_NCW.pdf>. Atkinson, Robert. 2004. The Past and Future of America’s Economy—Long Waves of Innovation that Power Cycles of Growth. Cheltenham, UK: Edward Elgar. Atkinson, Robert. 2006. “Is the Next Economy Taking Shape?” Issues in Science and Technology. Winter. Augustine, Norman. 2007. Is America Falling Off the Flat Earth? Washington, D.C.: The National Academies Press. Beinhocker, Eric D. 2007. Origin of Wealth—Evolution, Complexity, and the Radical Remaking of Economics. Cambridge, MA: Harvard Business School. Bennis, Warren, and Patricia Ward Biederman. 1997. Organizing Genius. New York: Basic Books. Berlin, Leslie. 2005. The Man Behind the Microchip: Robert Noyce and the Invention of Silicon Valley. New York: Oxford University Press. Blanpied, William A. 1998. “Inventing U.S. Science Policy.” Physics Today 51(2):34-40. Bonvillian, William B. 2006. “Power Play, The DARPA Model and U.S. Energy Policy.” The American Interest II(2):39-48. Bush, Vannevar. 1945. Science: The Endless Frontier. Washington, D.C.: U.S. Government Printing Office. Cebrowski, Arthur, and John Garska. 1998. “Network Centric Warfare: Its Origin and Future.” U.S. Naval Institute Proceedings January.
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