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Network Science 7 Creating Value from Network Science: Scope of the Opportunity In earlier chapters the committee discussed the definition, content, and research challenges of network science. In this chapter it focuses on how investments in network science can create value for the nation in general and for the Army in particular. CREATING ECONOMIC VALUE FROM RESEARCH KNOWLEDGE Investments in basic and applied research—that is, in “science”—create new knowledge. They also produce trained research personnel, and they may generate intellectual property (e.g., patents). They do not generate economic value delivered directly to an end customer or user. Rather, a long value chain of activities separates the creation of new knowledge at the beginning of the chain from useful commercial or military applications at the end of the chain (Duke, 2004). Therefore, knowing how the results of research might be used is central to assessing the ultimate value the Army can derive from supporting research in network science. The concept of real options analysis is helpful here (Amram and Kulatilaka, 1999; Boer, 2002; Mun, 2002). Applying options analysis to research activities allows for research to be viewed as creating the opportunity but not the obligation (here is where the term “option” comes in) to use the knowledge generated to create a final capability that a customer, in this case the Army, is willing to pay a supplier to create and deliver. These options can be valued using standard techniques of real options analysis (Mun, 2002). Many commercial firms plan their R&D investments using this methodology (Boer, 1999). While the committee does not pursue the full financial analysis discipline in this report, the underlying concepts allow it to assess the scope of opportunity available to the Army as it evaluates its investment alternatives for creating a new science of networks. This assessment is presented below. SCENARIOS FOR VALUE CREATION To explore the opportunity for different kinds of options that the Army might decide to create, the committee assumed that the Army will make finite investments in research to advance network-centric warfare (NCW) capabilities and constructed three scenarios that represent fundamentally different levels of investment. After briefly describing these three distinctly different scenarios, the committee presents its findings about how the Army can create value by supporting the development of network science. Since network science does not now exist, the committee had to make some assumptions about its future evolution in order to assess its potential value. All three scenarios involve moving targets, but the nature of these targets differs from one scenario to another. The descriptions that follow provide a sense of the direction dictated by each scenario and the choices available to the Army. Details of the scenarios are provided in Appendix E. Scenario 1, Building the Base Scenario 1 involves a modest level of funding (~$10 million per year) that fits into the Army’s current scheme for 6.1 basic research. Small amounts of Army risk capital funds are invested to create a knowledge and personnel base from which it can attack the practical problems that arise when trying to provide NCW capabilities. It is for this reason the scenario is called “building the base.” Because the anticipated investment is too small to fund significant interdisciplinary efforts, it should be focused on leveraging existing research in areas related to network science. As discussed in Chapters 5 and 6, the core of network science is rapidly evolving, and Scenario 1 would help it to crystallize. The committee envisions that the research efforts would be located at major research universities. An important as-
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Network Science pect of the program might be a once-a-year conference at an Army laboratory or facility, where the principal investigators (PIs) would report on accomplishments during the year. The program needs enlightened management to support interdisciplinary work accomplished through the interaction of a diversity of PIs. The essence of the program would be the achievement of fundamental advances in network research based on, among other things, statistical physics, applied mathematics, and the development of mathematical models of social phenomena by generously funding only exceptionally talented individuals who are collectively organized into a national network. Such a program would be the first to address the needs of network science per se. It would be devoted to the study of networks as coherent entities characterized by their architecture, structure, and dynamics. By deliberately adopting a broad theoretical and methodological focus, the program would encourage the creation of fundamentally novel ideas. A wide diversity of approaches can be a key feature of long-term success. Keeping the goals broad and flexible would allow the Army to cultivate such diversity, whereas narrowly defining the program would eliminate much of the creative potential for breakthroughs and new ideas. The Army’s needs are broad and fundamental in nature: It must learn how to approach the creation of a predictive description of large, interacting, layered networks. A basic science program is the first step toward building the critical mass of talent needed to address specific Army problems in this area. This modest approach would allow the Army to identify the relevant research community and organize it so that, in time, it could be called upon to address more specific needs. The proposed approach differs from existing programs in agencies such as the National Science Foundation (NSF) and the National Institutes of Health (NIH) in that it focuses on network science per se. While a significant amount of research is taking place in communities addressing the applications of networks, almost none of this research is funded by dedicated network science programs. As a consequence of its discussions with Army and DOD representatives, the committee has come to realize that the fundamental problems underlying effective network-centric operations (NCO) lie in the social domain. Yet how people interact and utilize technology or make decisions based on shared knowledge are areas almost unexplored in the Army’s current basic research portfolio. Applications to biology, engineering, and the physical sciences are also essential to Army applications, but the Army is already funding research in these areas. The committee suggests that, on the margin, the most significant problem is not how to build better satellites, tanks, or medicines, but rather how to organize millions of individuals to collect intelligence, deliver supplies, and prosecute wars over an increasingly global and constantly shifting geographical and political playing field (Garstka and Alberts, 2004). This is a monumental problem that has not, however, traditionally been the province of science. Rather it has been managed through a mixture of intuition, experience, and tradition. A significant fraction of the proposed program should address this organizational problem the way scientific problems are addressed: through a combination of theoretical modeling, data analysis, and controlled experimentation. In Scenario 1 (Appendix E) the committee indicates promising research topics in four broad areas: network structure, network dynamics, network robustness and vulnerability, and network services. Each area has theoretical, empirical, and experimental components. A basic research investment in each of these areas of network science would provide value for the Army. The committee also offers suggestions for improving the return on investment by modest changes in the way that basic research in network science is managed. Scenario 2, Next-Generation R&D Scenario 2 envisages applying best practices in industrial R&D management to the Army’s investments in projects that combine basic and applied network science. Specifically, the committee expects the objective of these projects to be the articulation of technology investment options that could be exercised by the Army and its vendors to provide a desired capability. The amount of this investment is envisaged to be between $25 million and $100 million annually, roughly $25 million per project. There are expected to be investments in the university community for the basic research and in both Army in-house activities and commercial firms for the applied research. The committee envisages, however, that the R&D projects would be managed in a way profoundly different from the way in which current Army in-house and external centers are managed. The selection of projects to be funded would be market driven and controlled by a top-level Army team. It is expected that connections between the basic and applied portions of the research will be much more intimate. Modern Internet collaborative tools would be used to manage the day-to-day work in rough analogy to the global design of industrial products. The activities are managed in small, intimate groups devoted to specific subprojects that are integrated into the overall project in a looser networked fashion. People flow from one small group to another over time. The entire team makes up a social network consisting of smaller, more tightly coupled social networks. In short, this scenario envisages the application of modern communications networks and tools and the insights of modern social network theory to transform the management of Army R&D projects. In Appendix E the committee provides details for market-driven management of such projects. The next-generation R&D model is a new and different approach similar to that of networked organizations like eBay, Intel, and GE. It is based on principles that have worked for many successful
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Network Science companies that needed to get quality products and capabilities to market quickly: Think Big, Start Small, Scale Fast, and Deliver Value. It is this type of next-generation model that can deliver the knowledge, research, and technology that will enable our warfighters to win the nation’s wars. The market-driven approach requires strong commitment from the Army and DOD senior leadership, and from their partners in industry and academia, to make it work. It will be led by a small team of the best and brightest Army “warfighting R&D specialists” committed for a period of 3 or 4 years, highly motivated, and working closely with industry and academia. The Army has an opportunity for leadership in developing and implementing this new model. At DOD, NCO must be a joint effort, and so should be the new R&D model—after it has been proved in the Army. By moving ahead aggressively to implement this model, the Army can establish itself as the lead for DOD and seize the opportunity to contribute significantly to the improvement of joint network-centric warfighting capability. The statement of task requests the committee to “identify specific research issues and theoretical, experimental, and practical challenges to advance the field of network science.” Briefly stated, three such issues were discussed in Chapters 3–6: Current military concepts of “net-centricity” are based on applications of computer and information technology that are far removed from likely results of basic research in network science. Current funding policies and priorities are unlikely to provide adequate fundamental knowledge about large complex networks that will advance network-centric operations. A basis for a network science is perceived in different ways by the communities concerned with engineered, biological, and social networks at all levels of complexity. A fourth issue, and major challenge as well, will be to obtain value from the investments that the Army does make to advance network science. In the case of basic research (6.1) alone, the relevant challenges are identified in Scenario 1. In the case of the combined basic and applied research (6.1–6.3) projects envisaged in Scenario 2, the challenges depend sensitively on the topics of the research. To illustrate the scope and scale of next-generation R&D projects with market-driven management, three projects involving the sociological, engineering, and biological areas of network applications were developed by members of the committee as sample projects for Scenario 2 and are contained in Appendix E. These projects were selected in diverse areas to underline the committee’s belief that research will be equally necessary in all areas to advance network science. The sample project in the social sciences domain outlines a study of local decision making in combat environments. It uses advanced information technology of the sort envisaged for NCO, with the goal of improving the quality of local decisions. The second project in the engineering domain proposes the design, construction, and testing of a large-area (roughly the size and complexity of a small city) monitoring network for both people and vehicles. The third describes the construction and testing of a prototype biological surveillance system to detect emerging biological threats. Such a system could also analyze the results of the surveillance and direct appropriate responses. While all of the sample projects have the potential to advance network science, they should not be construed as a “shopping list,” and the committee does not recommend their implementation without careful comparison of their costs and benefits with those of other research projects. Scenario 3, Creating a Robust Network-centric Warfare/ Operations Capability The statement of task instructs the committee to “recommend those relevant research areas that the Army should invest in to enable progress toward achieving Network-Centric Warfare capabilities.” When the committee examined the literature on this topic, it discovered that the concept of NCW has been superseded in the literature from the DOD Office of Force Transformation (OFT) by an expanded concept, NCO, as described in a conceptual framework document published on the OFT Web site1 (Cebrowski and Garstka, 1998; Garstka and Alberts, 2004). When members interviewed representatives from the Army and DOD, they found that opinions on NCW and NCO varied widely with regard to both nomenclature and substance. Moreover, the literature on the topic is dynamic, with many new reports and publications. Since this report is intended as an archival document, the committee elected to utilize the published conceptual framework description version 2.0 (Garstka and Alberts, 2004) as point of reference. Scenario 3 adopts a national point of view. Its purpose is to ask what the nation must do if the strategic vision of NCO is to be implemented. The committee was not tasked to resolve the issues raised in this scenario, but considers their resolution of paramount national urgency. The committee has stressed that the knowledge of networks that we possess today is not adequate to allow the design of predictable, secure, robust global networks. Members heard presentations and read reports of how the “transformation” to a future force capable of NCO is not likely to be achieved by traditional approaches to creating technology. The committee came to recognize that the policies and 1 For further information, see http://www.oft.osd.mil. Accessed on August 19, 2005.
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Network Science practices currently used to procure these capabilities do not take into consideration the uncertainties inherent in the current state of understanding the design and implementation of complex networks. The purpose of this brief scenario, then, is to emphasize that the task of designing, testing and operating the envisaged NCO capabilities is of an exceedingly high order of complexity and should be approached as seriously as the Manhattan Project or NASA’s race to the moon. The committee would be remiss in its responsibilities if it failed to note the essential urgency and profound difficulty of this task. The chances of delivering NCO capabilities in a timely and affordable way would be greatly increased by a focused national initiative, combining the initiatives of all services under central leadership, to respond successfully to the diverse challenges of future warfare. Transforming the U.S. military from its current state to that envisaged for NCO as described in the published conceptual framework version 2.0 is the probably the most complex undertaking in the history of the U.S. government (Garstka and Alberts, 2004). It is comparable to the successful pursuit of World War II and the Cold War with the Soviet Union. It is a long-term, difficult, costly, and risky undertaking. Start by thinking about the task of designing the most complex weapons system built to date—say, a large aircraft carrier. Add to this the complications in the physical domain associated with, for example, secure, reliable wireless communications via satellite to soldiers on a mobile battlefield. In the information domain, add the hardware and software challenges associated with storage, search, and retrieval of orders of magnitude more data in real time, as well as the challenges associated with ensuring the security and reliability of these data. In the cognitive domain, add the issues associated with a junior officer at a local (mobile) workstation processing information from sources at multiple levels in all military services. In the social domain, add the complications of orchestrating the decision-making process in this information-rich, real-time environment and the issues associated with tactics and training to use all this information-processing capability. The committee regards it as highly unlikely that existing methods of designing and procuring weapons systems will be adequate to accomplish this monumental task. Current experience in the services themselves supports this point of view (Brewin, 2005). Further, the committee regards the task of converting the current state of the U.S. military to the vision articulated for NCO as vastly more challenging than seems to be appreciated. Not only is the task dauntingly complex, the knowledge necessary to accomplish it does not even exist. In similar cases—the Manhattan Project and the initial days of NASA come to mind—a focused, long-term national initiative was required, and it seems likely that something similar will be required in this case also. Thus, Scenario 3 is one in which the United States undertakes a focused national initiative, comparable in scope to the Manhattan Project, to design and deploy NCO capabilities as described in the conceptual framework document 2.0 in all the military services during the coming decade. Implication of the Scenarios The main implication of the three scenarios is that there are multiple ways in which the Army can create value by supporting the creation of a science of networks. Which way it selects will depend on circumstances that the committee cannot know. Finding 7-1. The Army can create value in many different ways from a significant investment in the emerging field of network science. FINDINGS FROM SCENARIO 1 In Chapter 5 the committee discussed the rudimentary contents of network science. As is often the case, the empirical technology and engineering of large physical networks precede the scientific underpinnings of the technology. This is common throughout history. Humans were making tools and weapons from metals thousands of years before the science of metallurgy was developed. The situation is subtly different for biological and social networks, where the science is devoted to comprehending how these networks function. Tinkering with their natural engineering lies mostly in the future. The “technology” is well developed, but by nature rather than man. In all three cases—physical, biological, and social networks—the technology far outpaces the scientific understanding of what the technology hath wrought. Finding 7-2. Because network science is at an early stage of its development, a broad portfolio of basic and applied research is expected to create greater value than a more focused portfolio. Finding 7-3. If there is only a limited amount of funding (e.g., $10 million per year or less), a broad portfolio of basic research is the most promising approach to creating value for the Army. The main values created by a basic research investment include access to thought leaders (PIs) in the university community, training students through their work on university projects, the development of a community that the Army can access to address its practical problems, and efficient use of research dollars to impact multiple areas of application. Pursuing research in new ways also can generate value. In order to tackle complex problems, coordination is required. Yet individual insights gained by creative people are usually at the root of the solution of such problems. How does the Army get both at the same time? Network research suggests that small coherent groups associated with exceptional talents can be collected into loosely coupled networks
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Network Science that interact productively without sacrificing the creative potential of individual contributors (Malone, 2004; Watts, 2003). Creating, testing, and refining such an approach to network science by the Army would have far-reaching consequences in other domains. The Army’s network research portfolio must differ from the portfolios of NSF, NIH, and DOE (Department of Energy). This can be done if the Army focuses on the network per se, rather than on specific applications. Perhaps more important, the Army can explore new network approaches to complex problems along the lines indicated above. This notion is distinctly different from that of the centers of research currently pursued by the Army and NSF, among others. An essential element of the network approach, not normally present in centers, is the coherent, coordinated actions of a diverse group of different domain experts to address a precisely formulated, complex problem. This is not just the support of interdisciplinary and collaborative research. It further requires intensely focused attention on a pre-specified problem by diverse groups of domain experts working in concert. Suitable reference models include product development in large firms or the Manhattan Project rather than the efforts that take place at a typical Army lab, NSF center, or DOE user facility. The conceptual framework for NCO consists of interacting networks in four distinct technology domains: physical, information, cognitive, and social (Garstka and Alberts, 2004). The Army is currently investing primarily, if not exclusively, in R&D associated with a network communications infrastructure and a limited portfolio of applications built upon a network communications infrastructure.2,3,4 In other words, the current army R&D portfolio spans only two (the physical and the information domains) of the four domains essential to the implementation of NCO. Whether investments in physical and information infrastructure improve fighting effectiveness, however, depends on what warfighters do with the information available from the infrastructure. Their decisions and actions lie in the cognitive and social domains, which remain unexplored in the current DOD R&D portfolio. The good news is that existing knowledge in these domains could benefit applications in their respective domains of NCO. The bad news is that knowledge in these areas is rudimentary and generic. The insights are qualitative in nature and often not useful for making precise predictions (Malone, 2004; Watts, 2004).5 Serious investment in both basic and applied research is required before the associated models and concepts can be applied in a predictive way for the development of NCO capabilities for the Army. Finding 7-4. Since the shift to network-centric operations raises many social and behavioral issues, the Army’s network science portfolio should stress developing basic knowledge that could enable applications of network thinking to address the social and cognitive domains. Because the state of network science is so primitive, the central problems of the field are neither well recognized nor precisely posed. At this stage of its evolution, network science is basic research in the most profound sense: The fundamental questions are still being framed (Watts, 2003). Previous experience in other disciplines (e.g., Einstein’s contributions to relativity and quantum theory) suggests that this is a playing field best suited for talent of the highest order, not individuals doing next-step research. For the Army to create value from investments in this area, it must recruit and retain exceptional talent, a difficult task. Finding 7-5. The Army must find a way to attract the best researchers in network science. This will require stability of funding, the opportunity to interact with a diversity of interesting colleagues, and flexibility to follow the funded research wherever it leads. Finding 7-6. To attract the best researchers in network science, the Army should fund them to do work that also has applications in nonmilitary areas. Finding 7-7. To attract the best researchers in network science, the Army must avoid putting restrictions on publications and on foreign nationals. The committee is well aware that these three findings may appear to be as uncontentious as “motherhood and apple pie.” Sadly, this is not the case. In today’s global economy, outstanding technical talent has extensive international opportunities. Many of the world’s most talented people no longer wish to come to the United States. Many talented people here do not wish to work for the U.S. military. Plentiful opportunities exist for both elsewhere. To create value from basic network research, the Army must attract top talent. The committee believes that for the Army to have a good chance of success in this endeavor, it must heed the three findings. 2 S.W. Boutelle, chief information officer, Department of the Army, “The way ahead,” briefing to the committee on February 1, 2005. 3 J. Garstka, assistant director, concepts and operations, OSD Office of Force Transformation, “Fighting in the networked force: Insights from network centric operations case studies,” briefing to the committee on April 14, 2005. 4 J. Gowens and A. Swami, Army Research Lab, “Army research in network science,” briefing to the committee on February 1, 2005. 5 C.F. Sabel, professor of law and social science, Columbia Law School, “Theory of a real-time revolution,” briefing to the 19th EGOS Colloquium, Copenhagen, July 2003.
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Network Science FINDINGS FROM SCENARIOS 2 AND 3 Valuing technology and managing risks to extract economic value became hot topics in the business literature during the past decade (Boer, 1999; Branscomb and Auerswald, 2001; Chesbrough, 2003; Cooper et al., 2001). It is widely recognized in industry that product development is an inherently non-linear process, full of feedback loops and surprises (Branscomb and Auerswald, 2001; Reinertsen, 1997). Indeed, these same notions apply to DOD basic research (NRC, 2005). Thus, it is plausible to apply the notions developed in the business context to the management of research in network science by the Army. Finding 7-8. The Army can learn about R&D best management practices from the business sector. R&D managers in commercial organizations couple their basic research activities with known or anticipated applications. This provides focus and enables a much more rapid time to market (Branscomb and Auerswald, 2001; Chesbrough, 2003; Cooper et al., 2001). The committee believes that the Army could benefit from studying such practices and adapting analogous ones. Finding 7-9. Additional value can be extracted from the Army’s 6.1 basic research investments in network science by coupling them to downstream applied research and to technology development efforts. Finding 7-10. The results of basic research in network science are more likely to be rapidly put to use to meet Army challenges if the Army also devotes significant resources to related applied research. These findings are the basis for Scenario 2 in Appendix E, where the committee describes a management process and three sample research projects in the social, engineering, and biological spheres. All are based on and illustrative of current models and tools used for R&D management in the business sector, including software projects (Poppendieck and Poppendieck, 2003). The material presented in Scenario 2 in Appendix E describes how the Army might usefully experiment with new R&D management practices and new ways of integrating its 6.1, 6.2, and 6.3 R&D programs. The three sample projects developed in the scenario also serve to illustrate the truths of Findings 7-9 and 7-10. Although they can improve the performance of the front end R&D of the Army’s network design processes, commercial R&D best management practices must be supplemented by more fundamental change if DOD is to acquire NCO capabilities. R&D is only one step in a complete value chain. The military procurement value chain is more complex than the chains in commercial firms. Cases in which large commercial firms’ best practices for R&D management are known to work well are those in which (1) the science is mature and (2) the market requirements can be determined with fair accuracy. Neither is true in the case of applying the outputs of network science to the procurement of an NCO capability for the Army. Commercial best practices are most likely to succeed when they are applied to the sourcing of information infrastructure. Even here, however, prospects for success are not certain. There is no “science” at this time that can predict the performance of wireless and wireline communications infrastructures integrated into the architecture for the Global Information Grid (GIG). Science can predict the performance of individual components (e.g., of radios or computers) but not that of the overall system of networks. At best, the applications that warfighters are likely to develop for such a system are almost certain to be surprises conceived and tested in the field before they are embedded into tactics and doctrine. The current state of sociological models is too rudimentary for them to be applied reliably to simulate uses of such a network a priori. Even the use of simulation to determine the “market” requirements for the physical network is risky, because it lies beyond the scope of current knowledge. DOD faces a major challenge as it tries to determine how to design a set of interlocking networks of the complexity and scope envisaged for NCO. The committee captured its concerns about the total end-to-end sourcing process in the following finding: Finding 7-11. The design, testing, and deployment of the overlapping and interacting physical, information, cognitive, and social networks envisioned for network-centric operations concepts are currently beyond the Army’s capability. They require a concerted national effort to be achieved in a timely and affordable fashion. This finding motivated a third scenario, for creating a “robust network-centric warfare/operations capability.” It is a response to the committee’s hypothesis that the design and procurement of large, complex networks, such as those envisaged for implementing NCO, cannot be done in an affordable fashion using the current practices. The United States has faced similar challenges in the past—for example, the design of nuclear weapons in the 1940s (LANL, 1986). Responding effectively to such challenges required a focused, coherent, and sustained national effort involving government, industry, and academic partners and the investment of hundreds of millions of dollars annually over a decade or more. Moreover, recent insights on network organizations suggest that such a national effort must be organized and managed rather differently than a large engineering project (Malone, 2004). Scenario 3 is explored in Appendix E, but only in broad outline, because the committee was not consti-
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Network Science tuted to provide expert advice on this topic. Current knowledge on the organizational implications of network research leads the committee to suggest that the scenario illustrates the direction in which the military should head to obtain the biggest and most certain payoff from investments in network science (Malone, 2004).6 REFERENCES Amram, M., and N. Kulatilaka. 1999. Real Options: Managing Strategic Investment in an Uncertain World. Boston, Mass.: Harvard Business School Press . Boer, F.P. 1999. The Valuation of Technology: Business and Financial Issues in R&D. Hoboken, N.J.: Wiley. Boer, F.P. 2002. The Real Options Solution: Finding Total Value in a High-Risk World. Hoboken, N.J.: Wiley. Branscomb, L.M., and P.E. Auerswald. 2001. Taking Technical Risks: How Innovators, Managers, and Investors Manage Risk in High-Tech Innovations. Cambridge, Mass.: MIT Press. Brewin, R. 2005. DoD Mulls Network Coordination. Available at http://www.fcw.com/article88939-05-23-05-Print/. Accessed May 31, 2005. Cebrowski, A., and J. Garstka. 1998. Network centric warfare. Proceedings of the United States Naval Institute 24: 28–35. Chesbrough, H. 2003. Open Innovation: The New Imperative for Creating and Profiting from Technology. Boston, Mass.: Harvard Business School Press. Cooper, R.G., S.J. Edgett, and E.J. Kleinschmidt. 2001. Portfolio Management for New Products, 2nd edition. Reading, Mass.: Perseus Books. Duke, C.B. 2004. Creating economic value from research knowledge. Industrial Physicist 10(4): 29–31. Garstka, J., and D. Alberts. 2004. Network Centric Operations: Conceptual Framework Version 2.0. Vienna, Va.: Evidence-Based Research, Inc. Los Alamos National Laboratory (LANL). 1986. Los Alamos 1943–1945: The Beginning of an Era. LASL-79-78 Reprint. Malone, T.W. 2004. Network the Future of Work: How the New Order of Business Will Shape Your Organization, Your Management Style and Your Life. Cambridge, Mass.: Harvard Business School Press. Mun, J. 2002. Real Options Analysis: Tools and Techniques for Valuing Strategic Investments and Decisions. Hoboken, N.J.: Wiley. National Research Council (NRC). 2005. Assessment of Department of Defense Basic Research. Washington, D.C.: The National Academies Press. Poppendieck, M., and T. Poppendieck. 2003. Lean Software Development: An Agile Toolkit. Boston, Mass.: Addison Wesley. Reinertsen, D.G. 1997. Managing the Design Factory: A Product Developer’s Toolkit. New York, N.Y.: Free Press. Watts, D.J. 2003. Six Degrees: The Science of a Connected Age. New York, N.Y.: W.W. Norton. Watts, D.J. 2004. The “new” science of networks. Annual Review of Sociology 30(1): 243–270. 6 C.F. Sabel, professor of law and social science, Columbia Law School, “Theory of a real-time revolution,” briefing to the 19th EGOS Colloquium, Copenhagen, July 2003.
Representative terms from entire chapter: