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Defense Modeling, Simulation, and Analysis: Meeting the Challenge 2 The Changing Landscape The changing landscape and its associated needs for model-supported analysis, as described in this chapter and in Chapter 3, reveal three overarching themes: DoD needs MS&A appropriate to complex, dynamic, adaptive systems because such systems pervade military combat, other aspects of military operations, and other political, military, economic, social, infrastructure, and information (PMESII) phenomena of interest. DoD needs MS&A that is capable of effectively representing the ubiquity and significance of networking. DoD needs new methods to design and model real-time simulations coupled to embedded devices. This does not mean that all DoD MS&A should focus on complex, dynamic, adaptive systems or networks but means rather that DoD should consider them when developing and exploiting M&S for analysis and operations. Such considerations will affect the portfolio of investments, the terms of reference for individual projects, and the way in which integrative activities are undertaken. DoD’s future policies and practices in the development and use of MS&A should explicitly address the growing role and contributions of these three overarching themes. The remainder of this chapter elaborates on the changes faced by DoD. In Chapter 3, the committee makes specific recommendations for future directions in military MS&A and identifies the main challenges for MS&A in the upcoming decades. The modernization of DoD is a direct result of the changed environment in which it has been called on to operate since the end of the cold war. While military planners must continue to prepare for engagements with the armed forces of other nations, there is an increasing necessity to plan against insurgents and terrorists as well. The range of missions has expanded from force-on-force engagements to also include counterterrorism, stability and support operations, and humanitarian relief. This has had an impact on force structure, weapon systems, equipment, and personnel across all the services. The environments in which these missions are performed routinely involve urban areas in which large numbers of noncombatants are present. Clearly these missions cannot be successfully accomplished by DoD alone—they must be performed using all the elements of national power, including diplomatic, economic, social, information, and military power. These new realities have given rise to organizational changes that increase force deployability and change doctrine to include effects-based operations. This chapter examines the changed environment by looking first at new challenges that MS&A must be able to address and then at the new technological landscape that MS&A must represent. It concludes with a discussion of what is known about the return on investment (ROI) of MS&A in a military context. NEW CHALLENGES FOR RESOURCE ALLOCATION, PLANNING, TRAINING, AND OPERATIONS DoD’s Transformation Planning Guidance (DoD, 2003, pp. 8 and 19) poses new challenges for resource allocation, planning, training, and operations, while affirming the need for strengthening and expanding MS&A capabilities. It notes, in a paragraph on “transformed strategic analysis,” that the Department needs a transformed analytic capability that can identify and assess risks for strategic planning: … DoD must be able to support a capabilities-based planning process that accounts for greater uncertainty in threats and capabilities, and must be capable of comparing risks across time and between multiple theater-level operations. Later, in identifying M&S as one of the key elements of infrastructure for concept development, it notes that a new generation of M&S is needed to support concept development [including] linking together many types of simu-
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge FIGURE 2.1 The interrelationship among DoD processes areas. lations, from aggregate and detailed computer models to simulators and man-in-the-loop hardware components. Integration of Department of Defense Processes One of the challenges implied by that vision is the need for greater integration of DoD processes, which can be facilitated by improvements in MS&A. Three main DoD processes together lead to the development of military force capability: Concept development and capabilities definition. Currently called the Joint Capability Integration and Development System (JCIDS). Programming and budgeting. Currently called the Planning, Programming, Budgeting, and Execution process. Acquisition and engineering. Currently called the Defense Acquisition System and codified in DoDD 5000.1 and DoDI 5000.2. The capability output from these processes is then employed in a fourth process, the actual training and operations of the forces. MS&A supports each of these four processes, as illustrated in Figure 2.1. These processes cannot stand in isolation from one another but rather must be integrated to produce the overall capabilities. The cycle begins with concept development and capabilities definition specifying necessary force capabilities, which are supported through programming and budgeting, with the resources being used in acquisition and engineering to develop the materiel capabilities, which are then applied in the force through training and operations, with the output of this process then feeding back to a new cycle. Ideally, however, the interaction should be more complex than is shown in Figure 2.1, with numerous feedback loops. To cite one particularly important example, there must be continual iteration among capabilities definition, programming and budgeting, and acquisition to allocate resources. That is, capabilities definition must use the outputs of acquisition to incorporate the cost of developing a particular capability, which is then weighed against other costs in programming and budgeting to estimate the resources that can be allocated to the particular capability. If the full resources cannot be allocated, then capabilities definition and acquisition must interact further to examine trade-offs and determine a fallback position. This, in turn, may require another iteration with programming and budgeting if an appropriate fallback cannot be found with the given resources, and so on. Throughout this iterative process, MS&A provides the bond that enables the results of decisions made at each stage to be projected and used throughout. The role of the “consumer,” as defined in Chapter 1 in the section on the role of MS&A, is key throughout this iteration. In practice, the four processes are not well integrated in DoD. To address that situation, the 2006 Quadrennial Defense Review (Rumsfeld, 2006, p. 66) indicates that DoD is launching several initiatives to “integrate the processes that
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge define needed capabilities, identify solutions and allocate resources to acquire them” and will “reach investment decisions through collaboration among the joint warfighter, acquisition and resource communities.” To some degree, MS&A applications do currently support this integration. For example, mission analyses examine the levels of mission capability that are achievable for given resource expenditure, and campaign analyses examine trade-offs among overall components of force capability at given resource levels. However, the MS&A community can be a key partner in helping DoD achieve better and broader integration of its processes. To do so, MS&A must address a number of new challenges for resource allocation, planning, training, and operations. Capabilities-Based Planning A major challenge identified in the Transformation Planning Guidance (DoD, 2003) is the ability to move to capabilities-based planning (CBP). CBP stresses the development and honing of capabilities that can be applied to a wide range of tasks and circumstances. DoD is also moving toward an analogous capability for operations called adaptive planning, in which war plans are developed so as to anticipate and then support significant changes in those plans as circumstances change. The MS&A enterprise has an important role to play in CBP, but it must develop new capabilities in order to do that. Network-Centric Warfare Another major consideration for planning, training, and operations is network-centric operations. Networking is ubiquitous in all DoD activities, whether for peacetime planning or war itself. It is often referred to as network-centric thinking or network-centric operations—see, for example, Alberts and Hayes (2003), Alberts et al. (1999), and NRC (2000). By network-centric operations is meant military operations that are enabled by the networking of the force. Network-centric warfare (NCW) represents a powerful set of warfighting concepts and associated military capabilities that allow warfighters to take full advantage of all available information and bring all available assets to bear in a rapid and flexible manner. The tenets of NCW are these: A robustly networked force improves information sharing. Information sharing enhances the quality of information and situational awareness. Shared situational awareness enables collaboration and self-synchronization and enhances sustainability and speed of command. These, in turn, dramatically increase mission effectiveness. While there is considerable enthusiasm for the potential benefits of network-centric operations, practical experience is limited and the fundamental scientific base has yet to be built. As discussed in Chapter 3, there is an urgent need for research on how best to represent network-centric operations in models and simulations and how best to use MS&A in support of network-centric operations. Reconstruction and Stabilization Reconstruction and stabilization (R&S) is an important and often neglected phase of conflict and poses a new challenge for DoD. This period formally begins when (in the U.S. case) allied forces have satisfied military objectives to the point that sovereignty can be legitimately claimed. The R&S phase does not actually begin here, however, since planning and preparation for this phase must begin far in advance of execution. Successful reconstruction and stabilization depend significantly on successful control of many nonmilitary factors, from the provision of civil infrastructure to the management of political insurgency. Control implies more than the military enforcement of martial law; it requires integrated cooperation from foreign national agencies and from multiple agencies within the U.S. government. The time frame for accomplishing R&S has fundamentally changed. Today, U.S. forces are so proficient at conducting the military phases of conflict that precious little time is afforded them along the way to learn about the civilian fabric. In Operation Iraqi Freedom, for example, allied forces were sovereign barely a week after military operations were initiated. The R&S efforts of the military were taken by surprise. Two observations are key to the future of R&S. First, the roles and responsibilities of all the components must be defined and refined in advance, despite limited time available for preparing and conducting what-if exercises. Second, training and rehearsal capabilities are needed to support the activities required for R&S. The most promising technology-based approach to carrying out this task involves MS&A. Such capability would allow players from the various agencies to interact with one another, and it would provide a means by which planning, training, and evaluation could be standardized across the services in an era of evolving coordination. There are several modeling issues for R&S. What data do we need to assess regional stability? How do we use these data to distinguish between stable and unstable regions? How do we merge them with existing data and share everything with all of the interested, possibly nonmilitary parties? Can we quantify and measure changes in stability as data or parameters change? Finally, do we have all of the MS&A tools that we need? The problem here is that MS&A capabilities to aid planners in reconstruction and stabilization are sparse. There certainly are models that are useful for aspects of R&S. For
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge instance, engineering models used to determine quantities of steel or concrete that will be required for the civil engineering components of R&S are evidently useful, but they do not help in representing cultural, political (perhaps tribal), or other human considerations that are organic to R&S. There are attempts by the services (e.g., the Army’s Training, Doctrine and Analysis Command’s analysis center) and industry (e.g., Alion’s SEAS simulation product) to enhance current capabilities or to broaden them to satisfy some of the objectives that are assumed to be important for R&S MS&A. But there appears to be no product or near-term MS&A capability for planning or evaluating alternative courses for the vitally important R&S mission. On the other hand, the technological enablers in Chapter 3 (interoperability, composability, etc.) and the anticipated progress they are making suggest that MS&A for R&S is a distinct possibility. Within the government, DoD is arguably more technologically advanced in the relevant MS&A than other agencies involved in R&S and would be relied on to lead development of the technology that could be used by those agencies. Diplomatic, Intelligence, Military, and Economic (DIME) Options Because DoD is increasingly called on, as part of its evolving mission, to model the social and cultural aspects of conflict, it has an associated interest in developing models that go beyond the capabilities needed for R&S and take into account the entire range of political, military, economic, social, information, and infrastructure (PMESII) factors that might stem from, or guide, DoD decisions. This broadened range of DoD interests is consistent with the emerging viewpoint that DoD should consider an entire range of diplomatic, intelligence, military, and economic options when planning how to meet national goals. DIME is often used to designate a space of actions or options, whereas PMESII is used to describe a range of effects to be considered. Both stress the factors that contribute to and define the entire sociocultural environment surrounding the conflict. This vastly broader decision space puts heavy demands on M&S and the analysis that connects MS&A to decision makers. Ideally, DoD would like to develop the capability for modeling the entire range of factors and consequences that affect decisions, and in a way that enables easy evaluation of what-if scenarios and makes the assumptions and sensitivities readily visible. MS&A provides the only means for decision makers to gain experience and intuition about situations that have not occurred but that must be considered. A general-purpose MS&A tool kit, one that would allow examination of even a small portion of the entire DIME space in any sort of integrated way, does not exist. For instance, There is no evidence that the PMESII factors completely define what is meant by the sociocultural environment. There are no common, agreed-on ways of defining, representing, modeling, or measuring these factors. There is no single unified social science theory that covers all (or even a major subset) of these factors. The state of an actor or country in terms of these factors changes over time. These factors interact in complex ways to impact the behavior of U.S. and adversarial forces at all levels, from the individual to the nation state. From an MS&A perspective this means that, with respect to these factors, models at the current state of the art are incomplete. With rare exceptions, they are neither multitheoretical, multilevel, nor high-dimensional, nor do they deal with complex change dynamics. All of these attributes do not need to be present simultaneously for a model to be useful in MS&A, but more complexity than exists is needed for the problems cited above. Because there are no agreed-on definitions and representations, standard procedures for data collection do not yet exist. This lack of standard procedures, coupled with the inherent complexity and dynamics of the sociocultural domain, has two implications: The data needed to instantiate, tune, and validate these models are often nonexistent or at least woefully incomplete. The notion of validating models or at least establishing the conditions for their credible and responsible use must be completely rethought. The committee addresses both issues in Chapter 3, in the subsection “Expanded Concepts of Validation.” Effects-Based Operations and Effects-Based Planning Effects-based operations (EBO) is the name currently given to operations designed to achieve desired outcomes or “effects” through the synergistic, multiplicative, and cumulative application of the full range of military and nonmilitary capabilities at the tactical, operation, and strategic levels. The public literature on EBO includes Deptula (2001), Davis (2001), Smith (2006), and McCrabb (2005).1 Effects-based planning (EBP) is the staff process to work out the causal relationships and rationale for attacking various targets to support EBO. EBP balances that understanding with the known capabilities and risks. It is results-based as opposed to attrition-based, and it is much more specific than many classical expressions of a commander’s intent in describing the assumed linkages of actions to objects, often 1 Other information in this section comes from Maj Gen Robert Elder, Air War College, and Maj Gen Bentley Rayburn, U.S. Air Force, Briefings to the Conference on Effects Based Operations on January 31 and February 1, 2006.
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge through a sequence of intermediate effects. EBP closely mirrors the current joint planning process, yet focuses on the linkage of actions to effects to objectives. EBP changes the way we view the enemy, ourselves, and what is included and emphasized in the planning process. EBO poses substantial challenges for MS&A because of (1) the uncertainties about the actual effects of particular actions; (2) the inevitable learning and adaptation resorted to by adversaries, third countries, and other groups; and (3) the many options, influences, and uncertainties that must be modeled and tracked. As the committee emphasizes in this report, EBP cannot depend on accurate prediction and must rely on a combination of reasoned initial actions, observation, and rapid adaptation. To do otherwise, to depend upon predictability, is a recipe for failure. In planning, a premium is therefore placed on finding flexible, adaptive, and robust (FAR) strategies that anticipate and facilitate adaptation, and MS&A methods must support those strategies. Developing and analyzing alternative courses of action requires models that can represent complexity, adapt to situations as they arise, and explicitly account for the systems nature of most operations. As our MS&A capabilities improve, we are more able to represent some of the complexity of those defense systems that reflect these properties. These are described as complex adaptive systems (CAS).2 Models that incorporate this view also should try to represent potential adaptation of all parties, random developments that always afflict real warfare, and other factors and processes that relate to political, military, and economic actions. The emphasis on complexity must be reflected in adaptive models rather than the mostly scripted models that have been the mainstay of MS&A for many years. One promising approach for achieving adaptive models is agent-based modeling, although in forms beyond those taken by the most current work. Other modeling tools will be needed to incorporate human gaming and man-machine interaction, since it is commonly the case that expert human teams are better than models for suggesting innovative tactics and taking an integrative view. Over time, sophisticated models/agents might be developed that can substitute, to some degree, for such human teams, perhaps after such teams have first explored the concepts via games. The R&D needed for this is discussed in Chapter 3. Complexity also requires a more thorough understanding of, and procedures for, assessing and managing the set of relations that connect various PMESII entities. Tools that employ link analysis, social network analysis, and data mining can be a move in this direction. However, they are currently limited by issues of scalability, difficulties in dealing with missing data, massive data entry requirements, and attention to only one or two relations at a time. They are also hampered by a lack of shareable ontologies, an unwillingness to use text-mining techniques, and legal, policy, and control issues that arise in sharing information. Another dramatic feature of the future landscape for MS&A will be a substantial merging of MS&A into command and control systems. This is already happening as models used to monitor forces during execution and to conduct high-fidelity mission rehearsal become embedded in operational command-and-control systems. This trend will continue, bringing with it demands for MS&A that can reflect and routinely react well to real-time information that comes into the system during campaign execution. Continuous adaptation of plans will become more nearly routine. One simple current example is the launching of aircraft on missions before their targets are known. During the course of their mission, personnel may be directed to support a Special Forces team in trouble or the maneuver of an Army or Marine unit, to destroy time-sensitive targets that pop up from hiding, or to reattack fixed targets that were not adequately suppressed or destroyed by earlier attacks. Such on-the-spot adaptations may occur on a time scale of minutes. Representing such capabilities well requires high-fidelity models of a sort that were once associated more with training and exercises than with analysis and execution. NEW TECHNOLOGICAL LANDSCAPE Although a detailed examination of the new technologies that confront the military is beyond the committee’s scope, future MS&A must be prepared to deal with them. Here the committee briefly surveys the most important aspects of the changing technological landscape. Large Integrated, Interdependent Systems Historically, the services and, often, elements within them have operated with a large amount of autonomy. Recently there has been a shift to interoperable elements, with the goal of migrating to a more efficient set of mutually supportive capabilities without inappropriate redundancies. The legacy of rigidly defined systems (“stovepipes”) and their models is being rendered obsolete by emerging component-based systems. At the most abstract level, an example of this is the Joint Task Force (JTF) concept of a basic computer architecture that pulls together various components to provide the capabilities required for a particular mission. Some of these JTFs are larger than any single service could provide, encompassing capabilities beyond those organic to a single service, with information as the interchange mechanism. This is the basic, overly simplified concept of NCW. In essence, the information interchange mechanism is the middleware layer and the interacting systems are the components. In some instances, overall system capabilities 2 Some modelers use the term CAS to refer to the class of models, but in this report the committee uses the descriptor to refer to the real systems that those models seek to represent.
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge will be much more than the sum of the parts. This is familiar in the civilian world, where networking and distributed and collaborative activities are so ubiquitous. The deployment of these integrated and interdependent systems has rendered many of the existing stovepiped models obsolete for representing emerging capabilities. To reflect the new military capabilities, the models need to implement a component-based architecture with a defined set of middleware similar to these information systems. Several years ago, the Defense Modeling and Simulation Office tried to advance this concept for DoD as the High-Level Architecture (HLA) was being implemented. Although the need was clear, the concept was valid, and implementation was effective in many respects, some technical and managerial missteps occurred. For example, the original implementation of HLA was not robust enough to permit dynamic behavior that had not been preplanned in a simulation, thereby limiting applicability. Also, overzealous attempts to enforce the use of HLA significantly impaired some experimentation, such as that in preparation for Millennium Challenge 2002. Those promoting the newer Test and Training Enabling Architecture have taken a more focused and voluntary approach to implementation of a standard middleware layer, and the large community engaged in distributed simulation currently uses an assortment of protocols, including HLA. System of Systems System of systems (SoS)3 engineering deals with planning, analyzing, organizing, and integrating the capabilities of a mix of new and legacy systems into a new system whose capability is greater than the individual sum of its parts. SoS engineering is supposed to provide a comprehensive, collaborative, multidisciplinary, iterative, and concurrent technical management process encompassing the entire system life cycle, from the identification of systems capabilities through coordination of the development and integration of the parts, sustainment of the system, and system disposal. What makes SoSs novel is that rather than being single monolithic structures, they are composed of multiple, autonomous, interacting, and interoperable systems. Thus, SoSs tend to be larger and more complex than the legacy systems they replace. The movement to these large SoSs presents challenges for MS&A centered on scope and flexibility. Whereas previously system components could be narrowly defined and easily modeled, they now function more broadly with more complex models. SoSs are, by their nature, dynamic collections providing different capabilities at different times. Indeed, SoSs are expected to prove capable in circumstances that were not anticipated in any detail and were therefore not expressible in clear requirements of a classic sort. The models (and analyses) of these systems need to be able to adapt to requirements-driven changes and to circumstance-dependent demands in a timely and accurate manner. As can happen in any large complex system, the interaction of the parts of an SoS may cause the system to behave in a manner that was not planned. This emergent behavior is something that must be captured in the model so that it is not first observed during actual combat. The issue of bandwidth in a service-oriented architecture is a classic example—the performance measures of traffic are not simply a linear function of the individual performances. Relations between network loading, capacity, and performance must be modeled and analyzed before being tested under fire. Embedded Systems A number of MS&A issues deal with the difficulties of designing and modeling embedded systems. Many of these issues concern real-time simulations coupled to embedded devices (hardware-in-the-loop) and modeling dynamical systems. A further challenge is hybrid modeling, a combination of modeling continuous physical devices and discrete computational devices (most frequently discrete controllers). Progress has been made in this area of research over the last two decades. Another challenge is network modeling. As will be discussed in Chapter 3, many serious analytic challenges remain in order to address the size and complexity of modern networks. Unmanned Systems According to current plans, within the next 20 years almost one quarter of the entities within the battlespace will be unmanned. Given the state of the art, this in an extremely aggressive goal. What makes the goal even more challenging is the almost complete lack of cognitive infrastructure for understanding, much less modeling, command-and-control aspects of the training, mission rehearsal, and conduct of operations of such devices. Unmanned systems have a greater reliance on models than manned systems because the actions of the former are not as amenable to human intervention. Moreover, robotic entities will likely be deployed alongside soldiers as part of mixed-initiative teams. There is a clear need to understand how such teams could operate and how command and control can be exercised. As the technology for unmanned vehicles (UxVs) advances, these systems will become more prevalent in the battlespace. Unmanned aerial vehicles (UAVs) are currently in use for both reconnaissance and attack teleoperation. Unmanned ground vehicles (UGVs) are being used for explosive ordnance disposal. In these cases, the UxVs are controlled via a communications link to an operator (sometimes more than one) who controls the vehicle. While this takes the human 3 DoD guidance on SoS engineering can be found at http://www.deskbook.osd.mil/dag/Guidebook/IG_c4.2.6.asp.
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge FIGURE 2.2 Controlling a system requires knowledge of the environment and control of all of the layers shown here. out of harm’s way, it does nothing to reduce manpower needs. For that, a degree of autonomy will have to be embedded in the UxV. Assuming this can be done, questions arise that can only be answered by well-designed simulations: How will behaviors be developed? How is command and control going to be exercised? And most important, How is this new capability going to fit in with the organizational structure? In the behavioral stack shown in Figure 2.2, the bottom three levels depend on the specific implementation, but the top three can be implemented on an abstract model of the platform. With this in mind, the behaviors used in the simulation might well be transferred to the actual UxV. This would allow the tools that are used for planning and mission rehearsal to be used for execution as well. DoD is facing a new environment and new adversarial challenges. It will employ new technology and new management structures to deal with these challenges, with further advances already being planned. The current legacy systems of MS&A are insufficient to deal with these rapid changes. They deal with scripted, nonadaptive scenarios, take no notice of cultural factors in reconstruction, and do not recognize the network-centricity now prevalent. As part of this vision, agility in model development (probably through greater use of disposable and reusable models) is needed to reduce the cost of models. In the remainder of this report, the committee identifies steps DoD must take to realize MS&A that can meet these challenges. ESTIMATING RETURN ON INVESTMENT It is logical to ask why DoD should invest in MS&A. What is the benefit of that investment? A simple answer is that there is often no real alternative, because all military activity short of actual battlefield operations is, by necessity, a simulation, whether on an instrumented training range or in the CPU of a computer. But there are other reasons to invest in MS&A. Business models and military wisdom alike make it clear that MS&A provides methods and opportunities that realize a very positive return on investment (ROI). The ROI for any program can be evaluated in quantitative terms and, no less importantly, in qualitative terms, although such evaluation can be notoriously difficult. MS&A provides clearly exploitable ROIs from both perspectives. MS&A is routinely used to explore factors such as required end-strengths, retention levels, the ability to prevail in multiple simultaneous regional conflicts, and other aspects of military operations, including combat. Evaluating the ROI of MS&A in such roles involves weighing the costs against the benefits. Costs include the construction, operation, and maintenance of simulation capabilities. Benefits include time savings, improved training, and safety. Various business-based models, including discount rate, return on investment, and net present value, are used to support the decisions that are made. There are many concrete examples of ROI evaluations that have demonstrated cost savings or cost avoidances in the domains of training, acquisition, and force analysis. Examples come from all the services. Since the Navy has subsurface, surface, air, and land components, it is an appropriate service to use as an example. By one estimate, the Navy is expected to save approximately $130 million by using applications such as the following: AIM-9X. Flyout simulations (as opposed to live-fire exercises) developed for this air-to-air missile upgrade program will be used through FY09, resulting in more cost-effective test and evaluation. Distributed mission trainers. Joint, interoperable aviation simulations could provide savings of approximately $7 million per device over a 10-year period, according to Naval Aviation. Joint semiautomated forces (JSAF). Entity-level training simulation provides an alternative to the Navy’s war gaming system as well as to related constructive simulation systems and provides joint interplay capability for sea-based and shore-based combatants through the deployment of standard simulation communication protocols. JSAF is the simulation model of choice for the Joint Forces Command, Joint Warfighting Center. The quantitative ROIs provided by the above examples
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Defense Modeling, Simulation, and Analysis: Meeting the Challenge are compelling. But there are other analyses of ROIs that are viewed as qualitative. For example, Virtual at-sea training (VAST). This M&S system provides the ability to conduct simulated multithreat scenarios at sea using a tactical scenario distributed from a shore-based combat simulation center. This simulation capability provides the Navy/Marine Corps with an alternative to the live-fire bombardment of training ranges, thereby eliminating a significant political obstacle. VAST transitioned to the fleet in FY04 and has since been used to qualify more than 25 ships before deployment. It had a triple ROI: (1) it solved a severe readiness problem caused by the loss of the Vieques training range in Puerto Rico, (2) it reduced by an estimated one order of magnitude the costs of naval surface fire support qualification by substantially reducing the costs of steaming to achieve qualification, and (3) it helped maintain readiness in ship-to-shore fires while in transit or on station. The effort led to the technological enablement of other significant capabilities to maximize fleet readiness while steaming to and in objective locations at sea using Battle Force Tactical Training technology modified for at-sea use. USS Cowpens Combat Information Center Team Training (CICTT). The quantitative ROI for one exercise alone was significant. CICTT saved 4,000 barrels of fuel, reduced required military travel costs, and reduced in-port costs. But the qualitative results were arguably more important. According to the Navy, training was improved significantly because the crew was better rested and able to focus on learning. Training objectives were completed in much less time than the previous work-up cycle, specifically in achieving Emergency Surge Proficiency Levels I and II. Much can be and is said by the services about the qualitative value of MS&A. Prominent among the positive attributes of advanced simulation are flexibility and control. Globally networked simulators, some manned and others semiautomated, provide fidelity-managed scenarios for training and for actual combat rehearsal. Some of the advantages of this technology include the ability to stop a scenario, provide feedback, and reengage the mission. This capability can involve hundreds of manned players and thousands of behaviorally believable computer-generated actors. Scenarios can be varied on the fly and tactics can be trained at the individual, small group, or aggregate level. Tactics can also be changed in real time. Sentient red forces, played by tactically proficient coalition adversaries, provide instantaneous knowledge of results on the highly controllable, simulated battlefield. It is extremely difficult to quantify this advantage of MS&A. Both quantitative and qualitative ROI assessments clearly indicate the contribution of MS&A to the nation’s military objectives. Program managers and military commanders, all of whom must contend with budgets, are finding clear benefits in MS&A investment. The ability to quantify these benefits will be very important. By and large, military communities have traditionally resisted simulation in favor of live-fire training or evaluation, but this tendency will diminish as the technical quality of simulation improves. Many aspects of military operations—for example, the implications of ubiquitous networking, the implications of different types and degrees of information, and the potential political, social, and economic consequences of alternative courses of action—are not yet well understood, so M&S does not yet represent them well. Although much is known about counterinsurgency, and even about terrorism, techniques by which M&S can codify or apply that knowledge have not been developed. Although the past successes of M&S, partially enumerated above, support further development, quantitative justification would reinforce that support. REFERENCES Alberts, D.S., J.J. Gartska, and F.P. Stein. 1999. Network Centric Warfare—Developing and Leveraging Information Superiority. 2nd ed. Washington, D.C.: Department of Defense, Command and Control Research Program. Alberts, David S., and Richard E. Hayes. 2003. Power to the Edge: Command and Control in the Information Age. Springfield, Va.: EBR Inc. Available at http://www.dodccrp.org/publications/pdf/Alberts_Power.pdf. Davis, Paul K. 2001. Effects-Based Operations: A Grand Challenge for the Analytical Community. Santa Monica, Calif.: RAND. Department of Defense (DoD). 2003. Transformation Planning Guidance. Deptula, David. 2001. Effects-Based Operations: Changes in the Nature of Warfare. Arlington, Va.: Aerospace Education Foundation. McCrabb, Maris. 2005. Incorporating Effects into Military Operations, Air Force Research Laboratory Report AFRL-IF-RS-TR-2005-237. National Research Council (NRC). 2000. Network-Centric Naval Forces: A Transition Strategy for Enhancing Operational Capabilities. Washington, D.C.: National Academy Press. Rumsfeld, Donald. 2006. Quadrennial Defense Review. Department of Defense. Smith, Edward A. 2006. Complexity, Networking, & Effects-Based Approaches to Operations. Washington, D.C.: Command and Control Research Program, Office of the Assistant Secretary of Defense for Networks and Information Integration. Available at http://www.dodccrp.org/html2/pubs.html.
Representative terms from entire chapter: