3
New Challenges and Directions for MS&A

CAPABILITIES NEEDED FOR DEFENSE MS&A

In Chapter 2, the committee identified three overarching themes that must be reflected in DoD’s future development of MS&A: networking, adaptability, and embedded systems. These themes lead the committee to make three recommendations, which will be expanded on in this chapter:

Recommendation 1: DoD should give priority to developing flexible, adaptive, and robust MS&A methods for evaluating military strategies.

Recommendation 2: DoD should ensure that the basic architecture of MS&A systems reflects modern concepts of network-centric warfare.

Recommendation 3: DoD should give special emphasis to the development of MS&A capabilities that are needed within embedded systems.

These recommendations give rise to a set of functional needs. Because the new challenges in defense planning are dominated by uncertainty, solutions should emphasize strategies that are flexible, adaptive, and robust (FAR). That, in turn, requires an approach to MS&A that can help identify candidate FAR strategies and evaluate them. Adaptability is not a characteristic of most legacy systems, which include scripted (predetermined) data entities, strategies, tactics, and behaviors.

To elaborate, if DoD strategies and programs are conceived with branches and other features designed to cope with uncertainty, then the evaluation of options requires models that generate the realistic dynamic circumstances with which the strategic options will have to deal. Such models will need to reflect the learning, adaptive, and sometimes random behaviors of individual groups. They will also need to reflect the possibility of structural changes in the system as coalitions form or dissolve, key leaders emerge or disappear, and physical events change the realities of, say, geography or access. DoD can no longer evaluate its strategies with models conceived in a paradigm of well-defined closed systems. Adaptation can be achieved by drawing on methods derived from operations research, game theory, control theory, and agent-based modeling (see the subsection “Other Methods for Representing Adaptive Systems” in this chapter).

Another functional need is to ensure that people are employed effectively in the use of MS&A. One lesson learned over and over is that people are exceedingly capable when dealing with uncertainty or innovative concepts, or integrating across boundaries such as those associated with DIME and PMESII—indeed, often much more capable than traditional models and simulations. This superiority of human beings is so clear that, in some situations, gaming is a preferred method for operational planners and strategic planners. Gaming, however, has many limitations, including the potential for missing constraints imposed by the physics of the situation or by the real-world capabilities of systems. Further, humans have only limited capability to deal with complex nonlinear phenomena; they may be very creative and sometimes find novel solutions, but rigorous thinking amidst complexity is difficult. Furthermore, attempts to be rigorous often oversimplify the problem and obscure possibilities that are important, such as the diverse reactions of an opponent to one’s own actions. It follows that MS&A should be reconceived broadly to include human gaming and interactive M&S for originality and insight. This applies whether MS&A is used for the support of strategic planning or for training, operations, or acquisition. Although neither gaming nor interactive M&S is rigorous, they have demonstrated the ability to reveal important factors and possibilities often missed by individual analysts or decision makers.

Representing Complex, Dynamic, and Adaptive Systems

Previous generations of MS&A were developed largely with perspectives that we now associate with idealized prob-



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge 3 New Challenges and Directions for MS&A CAPABILITIES NEEDED FOR DEFENSE MS&A In Chapter 2, the committee identified three overarching themes that must be reflected in DoD’s future development of MS&A: networking, adaptability, and embedded systems. These themes lead the committee to make three recommendations, which will be expanded on in this chapter: Recommendation 1: DoD should give priority to developing flexible, adaptive, and robust MS&A methods for evaluating military strategies. Recommendation 2: DoD should ensure that the basic architecture of MS&A systems reflects modern concepts of network-centric warfare. Recommendation 3: DoD should give special emphasis to the development of MS&A capabilities that are needed within embedded systems. These recommendations give rise to a set of functional needs. Because the new challenges in defense planning are dominated by uncertainty, solutions should emphasize strategies that are flexible, adaptive, and robust (FAR). That, in turn, requires an approach to MS&A that can help identify candidate FAR strategies and evaluate them. Adaptability is not a characteristic of most legacy systems, which include scripted (predetermined) data entities, strategies, tactics, and behaviors. To elaborate, if DoD strategies and programs are conceived with branches and other features designed to cope with uncertainty, then the evaluation of options requires models that generate the realistic dynamic circumstances with which the strategic options will have to deal. Such models will need to reflect the learning, adaptive, and sometimes random behaviors of individual groups. They will also need to reflect the possibility of structural changes in the system as coalitions form or dissolve, key leaders emerge or disappear, and physical events change the realities of, say, geography or access. DoD can no longer evaluate its strategies with models conceived in a paradigm of well-defined closed systems. Adaptation can be achieved by drawing on methods derived from operations research, game theory, control theory, and agent-based modeling (see the subsection “Other Methods for Representing Adaptive Systems” in this chapter). Another functional need is to ensure that people are employed effectively in the use of MS&A. One lesson learned over and over is that people are exceedingly capable when dealing with uncertainty or innovative concepts, or integrating across boundaries such as those associated with DIME and PMESII—indeed, often much more capable than traditional models and simulations. This superiority of human beings is so clear that, in some situations, gaming is a preferred method for operational planners and strategic planners. Gaming, however, has many limitations, including the potential for missing constraints imposed by the physics of the situation or by the real-world capabilities of systems. Further, humans have only limited capability to deal with complex nonlinear phenomena; they may be very creative and sometimes find novel solutions, but rigorous thinking amidst complexity is difficult. Furthermore, attempts to be rigorous often oversimplify the problem and obscure possibilities that are important, such as the diverse reactions of an opponent to one’s own actions. It follows that MS&A should be reconceived broadly to include human gaming and interactive M&S for originality and insight. This applies whether MS&A is used for the support of strategic planning or for training, operations, or acquisition. Although neither gaming nor interactive M&S is rigorous, they have demonstrated the ability to reveal important factors and possibilities often missed by individual analysts or decision makers. Representing Complex, Dynamic, and Adaptive Systems Previous generations of MS&A were developed largely with perspectives that we now associate with idealized prob-

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge lems and mass-dominated forms of warfare emphasizing attrition and, sometimes, maneuver. This style reflected the educational background of the model builders and the experience of the United States in two world wars. These were “industrial wars,” and the U.S. style in war was reasonably described as winning through sheer overwhelming force with large military forces and prodigious quantities of aircraft, ships, and tanks (Weigley, 1973). In addition, the individual services fought separately and had clear sectors of responsibility, which simplified deconfliction. Finally, the U.S. military thought about war as combat itself, with relatively little discussion of gray area activities before, during, and after combat.1 Over the last 15-20 years, the shortcomings of that approach to MS&A have increasingly been recognized, and the skyrocketing power of computing has made richer approaches possible. As far back as 1980, a new approach that would be, in today’s terminology, more joint and more integrated with both political and military considerations was being considered by DoD. The Office of Net Assessment sponsored an ambitious undertaking along those lines at RAND. With the end of the cold war, the disappearance of the Soviet threat, and the temporary loss of interest in big models and games, the effort dissipated despite its successes, although leaving behind the improved Joint Integrated Contingency Model (JICM), which is now an important element of analysis in parts of DoD. The effort also stimulated a great deal of research into planning under uncertainty, which has contributed significantly to today’s concepts of capabilities-based planning (CBP) and adaptive planning (the phrase used by DoD when referring to operations planning under uncertainty). During those same 15-20 years, researchers in a number of scientific disciplines were making progress in modeling complex adaptive systems (CAS). Chemists, physicists, biologists, economists, engineers, and others nurtured and shaped CAS theory as a powerful way to look at much of what goes on in the real world. The approach to modeling CAS includes systems of interacting actors at different levels of organization, actors that have goal-seeking behavior and that can learn, adapt, and interact in ways that sometimes lead to higher-level phenomena (emergent behavior) whose character is not predictable by viewing the individual actors.2 This extended earlier thinking about systems, such as the system dynamics methodology pioneered at MIT by Jay Forrester,3 and added an emphasis on nonlinearities and an increased ability to predict events that were sensitive to initial and subsequent conditions.4 By recognizing this fundamental fact—that is, that the real world is a complex adaptive system—one’s approach to MS&A changes substantially. Table 3.1 displays some of the changes. The first column applies to relatively simplistic views and models; the middle column applies to a few defense models of the 1980s, such as the RAND Strategy Assessment System (RSAS), Eagle, and the Navy Simulation System (NSS); and the last column arguably applies to the future. If the table is roughly correct, then the conclusion is inescapable that representing jointness, DIME/PMESII aspects, and asymmetric strategies requires modeling that is mindful of the paradigm of complex, adaptive, and dynamic systems. Moreover, that conclusion does not depend on whether particular methods of current mainstream CAS research prove enduring. Table 3.1 might suggest an inexorable movement toward complexity—that is, a move from simple models to something inherently deep and detailed. There may, in some respects, be such a movement, but movement toward complexity is not what this report recommends. Instead, the committee sees the need for families of models and games varying greatly in level of detail and perspective. Perhaps most models should be relatively small, specialized, and readily understandable, with only a few models and simulations used for integrating concepts, as described in the last column of the table. The model-family concept is discussed further in the section titled “Promising Technical Approaches for Attaining the Needed MS&A Capabilities.” The importance of relatively simple models is also discussed there and elsewhere in this chapter. Features mentioned in the intermediate column were achieved to a significant degree in some 1980s-era systems, such as the RSAS, the Eagle, and the NSS and to some extent in the Joint Warfare System (JWARS) system currently being tested. Representing Embedded Systems A special challenge in representing complex systems arises from the need to develop MS&A capabilities that are part of embedded systems. In such cases, there might be feedback loops, dynamic incorporation of new data, and complex interactions between real (sensed) and simulated data. The state space becomes enormous, yet quality assurance must be high. There are many unresolved challenges as a result. Although embedded MS&A capabilities have existed as an integral part of many systems, especially DoD systems, 1 See Hughes (1989) for a good discussion of U.S. military modeling into the 1980s. 2 See Waldrop (1992) for an excellent popular-level account of the Sante Fe Institute’s early work. See also Holland (1995) for an excellent scientific discussion that can be understood without mathematics. 3 For a more recent account of system dynamics, see the text by Sterman (2000). 4 For a theoretical treatment of the connection of dynamical systems and simulation, see Nagel et al. (1997, 1999).

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge TABLE 3.1 Levels of Model Sophistication Aspect Simplistic Intermediate Advanced View of war Continuous “piston- driven” battles. Simple system depictions with with air, mari- time, and ground components. Maneuver leading to discrete battles with attrition and move- ment affected by material and qualitative considerations. Richer logistics and depiction of political-military systems, with some models represent- ing decision makers’ war plans. Preparation, combat, and stabilization and reconstruction, substantial political and economic aspects. Allow big shifts of war trajectory as result of changes in leaders, coalitions, forces, or events. Logistics with just-in-time and responsive aspects. Number of parties Two sides, with allies folded into the appropriate side. Plus some explicit modeling of third countries. Plus nongovernment organizations and threats. Decision making and strategies Implicit in data or behavioral algorithms in specialized models. Top-down decision models, political and military branches and adaptations. Top-down, bottom-up, and distributed decision making and behavior at all levels, sometimes emergent. Instruments Physical attrition and targeting. Plus some mechanisms for escalation, de-escalation, or or termination. Plus nonkinetic attacks and mechanisms of coercion and dissuasion. Attrition and targeting mechanisms Difference equations with situational coefficients; direct physical destruction. Plus per-sortie or or per-shot kills, breakthrough effects, and other embellishments. Plus nonkinetic kill mechanisms and effects-based operations. Nature of variables Only objective variables, such as a side’s firepower. Plus soft variables such as a side’s fighting effective- ness, affected by morale, leadership and other factors. Plus soft variables such as nationalism, ethnic group association, and propensity for brutality and terrorism. Command and control (including information assurance and intelligence) Assumed perfect. Plus specialized technical models of communicat- ions. Plans pre- determine who does what to whom. Plus network-centric concepts with, e.g., publish-subscribe architectures and capacity for self-synchronization. Intended purpose of model runs Emphasis on predictive modeling. Some sensitivity. Multiscenario analysis with recognition of great uncertainies. Exploratory analysis in search of strategies that are flexible, adaptive, and robust.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge their number and diversity have greatly increased, from handheld devices to spaceships, and hence vary greatly in the constraints on computational power, memory management, and timing requirements. The projected use of unpiloted ground and air vehicles or swarms of vehicles that can respond autonomously to environmental changes or changes in enemy actions observed by onboard detectors is a prime example of the importance of embedded systems to future military capabilities. In order to effectively respond to changes, these vehicles need to autonomously project the effect of their courses of action. Despite the widely varying scales and capabilities of embedded systems, most embedded systems have the same impacts on MS&A. Most prominently, in an embedded system, the MS&A has gone from being an offline computational capability for reasoning about the system to being an integral part of a performing system in an online, often real-time capability. This mode of operation for MS&A requires new types of traceability, new ways to provide checkpoints for the system so that it can back up to previous decisions and states within a rapidly changing circumstance, and new ways to evaluate and report partial results amid ongoing computation. It also requires new self-monitoring and self-analysis capabilities that provide, for example, a computationally reflective MS&A system that monitors its own state and acts on and modifies itself. Because embedded systems must use the currently available data within a fixed amount of time and must therefore sometimes yield intermediate results, we will need advances in methods for evaluating the goodness of the current solution or the computational progress so far—that is, how far the current solution is from the optimal and how much additional value can be obtained by continuing computation. M&S embedded within a command and control system exemplifies what is generally known as a dynamic, data-driven application system (DDDAS). Such systems are the target of a major program at the National Science Foundation.5 In such a system, the embedded M&S is expected to control and guide a measurement process, determining when, where, and how to gather additional data. The embedded M&S must operate at both a global level—determining which systems to use to collect more data—and a local level—guiding particular systems as they gather measurements. The vision of a DDDAS-style system also includes a second major goal, the incorporation of dynamic data inputs into an executing application in order to have the currently most accurate data available for models and other computational processes. This vision includes the ability of the system to accept and respond to dynamic inputs from live data sources that might include users, computational processes, archival data, or sensors. One of the hardest challenges in such systems is the reconciliation of the modeled world with a continuous stream of newly measured data. To the extent that this challenge can be met, the advantages are clear for a large number of current MS&A applications, especially in time-critical applications such as route planning. Embedded systems that incorporate simple forms of MS&A are already tackling this hard issue of reconciling and updating their models with newly sensed information. However, the state of the art requires a lot of hand-tuning, and there has been little analysis or validation of these early systems. Representing Networking Through most of the 1990s, DoD’s models and simulations were largely developed with the technical aspects of command and control taken for granted or, at best, treated by separate communities, such as those dealing with communication issues or space surveillance. Within most combat models, command and control was largely assumed to work, except perhaps for parameters representing delays and hard-wired relationships allowing only some organizations to communicate with others. The mental picture of command and control was often point to point, and if one had a specific problem in mind, such as requiring the presence of a particular surveillance platform, that platform could be added with specific point-to-point links. The modern concept of networking is quite different from this point-to-point perspective. Some of the capabilities enabled by the increasingly ubiquitous presence of networks and associated services are the following: (1) planners can draw information worldwide without knowing the specific locations where the information resides, (2) operating forces can obtain situational-awareness (sensor and intelligence) information without hard wiring sensor-to-weapon-platform links, and (3) flexible command and control and organizational relationships can be established to meet the needs of immediate contingencies and then to adapt as the situation evolves. This type of networked world and the concomitant network-centric operation suggests the need for a new generation of MS&A having basic architectures in tune with modern-day concepts. Such architectures would probably be quite different from the prenetworking architecture, which had a simple overlay of particular platforms for sensing and communication. Some strides are being made, but the committee believes that no clear consensus yet exists for how network-centric operation should be represented in DoD’s MS&A, either as retrofits to legacy models or in future models. The need for this new MS&A capability is critical. It is required to help develop operational concepts and force structures for U.S. and coalition military forces, allowing them to meet and adapt to the new threats facing them, particularly in light of uncertainty about where and in what manner threats will arise. Furthermore, the DoD investment 5 Information available online at http://www.dddas.org.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge in developing network-centric capabilities is large—the expenditure estimated for the Global Information Grid (the emerging networking and information infrastructure) through 2011 is $34 billion (GAO, 2006), and analytical means are necessary to optimize this and later investments. The following examples are representative of the current state of network-centric M&S for the major DoD process areas shown in Figure 2.1.6 Concept development and capabilities definition (example 1). Agent-based models with simple agents (i.e., few rules governing their behavior) have demonstrated qualitative behaviors of network-centric operation (e.g., self-synchronization of force elements). As such, these models can serve as exploratory tools, but they do not provide the quantitatively supportable results needed for more detailed analysis. Concept development and capabilities definition (example 2). Numerous experiments involving live and simulated forces have been conducted to explore network-centric operational concepts. While a variety of models and simulations support these experiments, they themselves do not typically embody network-centric concepts—rather, the network-centric behavior is achieved by connecting the individual models and simulations over physical networks. Programming and budgeting. Traditional models (CASTFOREM, VIC, NSS, and others)7 have been used by the military services in making major resource allocation decisions affecting the development of network-centric capabilities (e.g., for the Army’s Future Combat System). While the services indicated they had made significant progress in the course of these analyses, these activities were assessed as being only initial efforts in addressing network-centric operations (MORS, 2004). Acquisition and engineering. Network Warfare Simulation (NETWARS) is the Joint Staff’s standard model for measuring and assessing information flow through existing and planned military communications networks. It is intended for analyzing performance resulting from behavior at the physical layer through the network layer, and it provides an extensive capability for doing so. However, its focus on the lower layers of the network precludes it from modeling important technical factors such as information assurance (beyond encryption), higher-layer services (e.g., discovery and collaboration), and ad hoc entry to and exit from networks that are critical to envisioned modes of network-centric operation. Training and operations. Agent-based models coupled with the techniques of dynamic network analysis have been applied to address operational problems confronting combatant commands. Examples include assessing and improving the organizational behavior of command and control staffs and characterizing the network structure of terrorist threats and their surrounding social environment. While these examples illustrate how the underlying technologies contribute to operational use, much basic research must be done and empirical data collected for a broad, robust application of the technologies. In summary, there is activity in each of the four DoD process areas shown in Figure 2.1, but in none of them is there yet a broad conceptual basis for addressing the problems of the area. Given the need for MS&A to represent network-centric operations and the current state of such representation, as indicated by the examples above, the committee recommends as follows: Recommendation 4: DoD should establish a comprehensive and systematic approach for developing the MS&A capabilities to represent network-centric operations: Enhance and sustain collaborations among the various parties developing network-centric MS&A capabilities. As it researched the examples given above, the committee found little evidence of significant interaction and cross-fertilization across the application communities associated with each of the examples. Such interaction is necessary to promote innovation and establish broadly based capabilities. The necessary collaboration might be facilitated by a DoD-sponsored series of workshops involving all the communities, leading to a substantive report synthesizing the views of the different communities and identifying opportunities for cross-fertilization. Continue and extend the development of existing approaches to modeling network-centric operation. Since the basic architecture and functioning of traditional models reflect a prenetwork perspective on military operations, those models are not adequate for describing network-centric operation. Still, they cannot be abandoned at this time since no other means exist for supporting certain important quantitative analyses such as resource allocation. Agent-based models have shown some promise, and 6 A major capabilities-based analysis of network and services infrastructure (the so-called Net-Centric Operational Environment) sponsored by the Joint Staff and being carried out by the RAND Corporation was planned for completion in May 2006. When completed, that analysis should provide a further example of the application of modeling and simulation to network-centric operation. 7 CASTFOREM, Combined Arms Task Force Engagement Model; VIC, Vector in Commander; and NSS, Naval Simulation System.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge TABLE 3.2 Important Directions Recommended in This Report for Advancing the Capabilities of Defense MS&A Area Recommended Topic Page Navigation of large state spaces Exploratory analysis 23   Multiresolution modeling 24 Representing complex adaptive systems Optimization and agent-based models 25   Multiagent systems 26   Social behavioral networks 27 Fundamental scientific issues Serious games 28   Network science 29   Embedded MS&A systems 29   Expanded concepts of validation 30 Infrastructure needs Composability 32   Improved data collection for MS&A 32   Visualization of high-dimensional data 35 further development is warranted. Attention should be given to the use of complex agents with sizable rule sets governing behavior to provide quantitative models and to the continued coupling of agent-based models with the techniques of dynamic network analysis (for a fuller discussion of dynamic network analysis, see the subsection “Social Behavioral Networks,” later in this chapter, and see Appendix B). In contrast to bottom-up agent approaches, a top-down architectural approach to describing networked behavior is also desirable, but no good examples of such an approach exist yet. Establish a new mathematical basis for models describing network-centric operation, drawing on an array of approaches, particularly complex, adaptive systems research. Just as new mathematics or the new application of existing mathematics has been necessary for advances in science, so too might a new network-based mathematical framework be necessary to realize appropriate models and simulations for network-centric operations. In general, research in the complex adaptive systems community could provide a basis for this framework. Some ideas along these lines have been put forward based on the mathematical structure of networks (Cares, 2006), and the methods underlying dynamic network analysis should also be applicable (Carley, 2003). PROMISING TECHNICAL APPROACHES FOR ATTAINING THE NEEDED MS&A CAPABILITIES To build the capabilities described in the preceding section, progress is needed in four areas of MS&A: Tools are needed for navigating intelligently through the very large state spaces characteristic of complex, dynamic networked systems. Some promising directions are described below, in the subsections devoted to exploratory analysis, multiresolution modeling, and families of models and games. Methods are needed for representing complex adaptive systems. There has been real progress in this direction through agent-based modeling, other means of representing adaptive systems, serious games, social behavior networks, and network science. Research must be stimulated to address fundamental questions that limit our ability to create scientifically sound and empirically grounded MS&A of the necessary complexity. There are many open questions about the analytical basis for such complex models, and validation is still more of an art than a science. New requirements for MS&A will need capabilities based on new infrastructure. In the rest of this chapter, the committee recommends directions for advancing the capabilities of defense MS&A, as shown in Table 3.2. Many of these topics are already recognized within parts of DoD as logical steps for the evolution of MS&A, and relevant R&D exists or is beginning. Therefore, the table should not be taken to imply that these are new or unappreciated ideas; rather, it demonstrates an interwoven vision of the many important topics in the vanguard of defense MS&A and recommends that DoD and the MS&A community adopt this holistic view for advancing DoD’s capabilities to address the needs identified in Chapter 2.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge FIGURE 3.1 Divergent and convergent thinking in search of FAR strategies. Exploratory Analysis MS&A is needed to assess, among other things, whether an option under consideration is flexible, adaptive, and robust (FAR). The option must be evaluated across as broad a space of scenarios as can be conceived. In addition to having suitable models and games for such an evaluation, one must also have methods for generating cases throughout the space, be able to use M&S to characterize results from each case, and then be able to make sense of the results. Figure 3.1 provides a schematic depiction of the kinds of thinking involved in developing and evaluating FAR strategies The sheer dimensionality of the space of possibilities can make FAR strategy development a daunting task. A rich collection of methods for exploratory analysis has developed over the last decade or so.8 These include methods for structuring the initial possibility space, divergent thinking to expand notions about what is possible (to include, for example, the possibility that people and groups will adapt or that events that change the very structure of the system will occur), generating simulations, visualizing outcomes, and applying a variety of tools—some of them derived from data mining or cluster analysis, some from the artificial intelligence and statistics communities, and some from outside-the-box gaming or brainstorming—and then finally converging toward reasonable depictions of alternative strategies and assessment of their merits. Exploratory analysis is very different from traditional sensitivity analysis. Whereas sensitivity analysis typically examines how the outputs of a complex model or simulation change when parameters and inputs deviate slightly from nominal values, exploratory analysis typically attempts much broader coverage of the space of parameters and inputs from much simpler quick-and-dirty models. Different versions of exploratory analysis apply to planning and programming system development, and operations, and the difference between the versions is large.9,10 Some of the challenges associated with exploratory analysis are deeply technical while others have to do with how best to structure collaborative analyses involving both hu- 8 Exploratory analysis is discussed in a larger context in Davis et al. (2005). An early reference drawing upon a decade of work on multiscenario analysis is Davis (2003). For discussion of the related method of exploratory modeling in the context of long-range planning, see Lambert et al. (2003). 9 Some DoD applications are discussed in Johnson et al. (2003). 10 Exploratory analysis is not like sensitivity analysis as it is ordinarily practiced, varying one or two parameters at a time around some base case. It involves exploring the entire space implied by the domains of the model parameters. Because of massive uncertainties, exploratory analysis often rejects even the concept of best-estimate base case. Modern statistical analysis sometimes examines the entire space of a small, well-defined model but seldom looks into uncertainties about the structure of the model itself.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge mans and machines and how best to summarize results for decision makers. While some decision makers prefer firm predictions and dislike uncertainty, many have a great interest in understanding uncertainty and how their course of action can both allow for opportunities that may arise and hedge against downside events. The issue becomes how to convey that kind of information clearly and accurately, a topic discussed further in Chapter 4. Multiresolution Modeling and Families of Models and Games A multiresolution model (MRM) is a model that can accept inputs and/or perform analyses at varying levels of resolution. True multiresolution modeling is very different from modifying a bottom-up model that requires high-resolution inputs to enable it to display aggregated (low-resolution) outputs. One motivation for MRM is the recognition that people need to reason at different levels of detail. At any given level of decision making, people do most of their reasoning with the natural variables of that level. In addition, they need to be able to zoom to the next more detailed level, so as to understand factors and phenomena underlying these higher-resolution features. Furthermore, they typically need to be able to summarize their reasoning to their own superiors, abstracting it into a form suitable for a lower-resolution level. The need for models at different levels of resolution cannot be addressed by simply applying sufficient computing power to do all modeling at the highest resolution. There are fundamental issues associated with aggregation and drill-down that must be understood and incorporated into models if multiresolution models are to provide effective support to decision makers. To some extent, a given model can be designed so that it can be used at different levels of detail. However, because MRM models can become quite complex, at some point it becomes easier and clearer to have an integrated family of models. Sometimes it is adequate to have a family of models that were not designed in an integrated way but that are sufficiently consistent and sufficiently well understood to be able to inform one another. For example, if one has a trusted high-resolution model, it can be used to develop values, value ranges, or even probability distributions for the inputs to a higher-level, more-aggregated model of the family. Further, if one has a trusted low-resolution model, perhaps informed by solid empirical experience (including history), it can be used to inform higher-resolution models. Sometimes simple models reflect considerations such as the morale and fighting effectiveness of a nation’s army, which have usually been assumed away in higher-resolution models.11 Exploratory analysis is arguably best accomplished with a good aggregate-level model that can cover the entire possibility space clearly, albeit at low resolution. Such a model might have 6 to 10 variables. Understanding the outputs of the model over the entire space the values of those variables is quite feasible with modern tools. Further, one can then reason at that level of detail. If one does such a synoptic exploration and finds that only two or three of the variables are particularly important, then with MRM or a suitable family of models, one can zoom to higher resolution on those variables. This provides a straightforward, cognitively natural way of conducting exploratory analysis. In contrast, if one starts with a complex model built bottom-up, the model may have thousands of inputs (especially if one realizes that the individual items in the model’s complex databases are all uncertain). Making sense of that model’s behavior and finding abstracted insights can be exceedingly difficult and treacherous.12 Thus, while MRM is not necessarily essential for exploratory analysis, it is a strong enabler. Another enabler for exploratory analysis (again useful for other purposes as well) is having families of models, human games, experiments, and other sources of information (Davis, 2006). Figure 3.2 illustrates this by suggesting the strengths and weaknesses of some of the various instruments that can be brought to bear. Although the cell-by-cell evaluations depend on various assumptions and are only approximate, the story conveyed is valid. For example, relatively simple analytical models and programs (top left) are excellent for agility and breadth of work and for highlevel decision support but poor for revealing underlying phenomenology. In contrast, detailed models, bottom-up, agent-based modeling (discussed later), human games, field experiments, and history can be very good at representing and studying phenomenology. Human games and man-machine gaming can also be particularly good for coming up with innovative concepts of operations, clever tactics, and new uses of technology. Strategic-level simulations can be excellent for integration, especially if they have adaptive decision models. Recommendation 5: DoD’s analytical organizations should take a portfolio approach to designing their analyses and supporting research, investing in a range of methods including diverse models, games, field experiments, and other ways to obtain information. 11 See Dupuy (1987). Such insights are reflected in some models, such as JCIM and, more recently, JWARS. 12 In some cases, the detailed model can be exercised with a statistically designed experimental plan, and its outputs can be analyzed statistically. It may be that despite the model’s complexity, only a few variables matter, or that only a few composite variables matter. In other cases, however, there are subtle and complex interactions among the variables that make statistical analysis either difficult to construct or hard to interpret. See Davis and Bigelow (2003) for discussion.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge FIGURE 3.2 Relative strengths for MS&A models. Such organizations should be cautious about (1) allowing the high-cost MS&A activities to use all of the investment resources, with no groups doing fast, simple, and nimble thinking; and (2) depending entirely on computer models, which may be unrealistic because they lack human involvement and often do not use real-world data, such as lessons-learned information from recent wars. If exploratory analysis were used by analytical organizations in cooperation with other organizations, something else would probably happen: increased analytical structuring of human games and exercises. Games and exercises are rarely designed with the idea of building a consolidated knowledge base. But they could be, in which case human games would be tailored and analyzed accordingly, as would experiments, resulting in enhancements to a knowledge base and to models. For example, a theater-level model could have sub-models to represent commanders’ decision making. Those, in turn, could be made to address issues arising in human war games (as well as many other issues not arising in the games). In practical terms, this might involve building into the model aspects such as substantial decision delays except in circumstances of prior alert and prior authorization for rapid action. Decision delays might also be explicitly dependent upon the availability and quality of information that is not from satellites or aircraft but from U.S. national and regional intelligence, personal commander-to-commander conversations with allied officers, or special information from, say, a nongovernmental organization aware of circumstances on the ground. That is, human gaming could force MS&A to incorporate variables that are important in the real world of DIME/PMESII but not natural to those building traditional mathematics-based models. Optimization and Agent-Based Modeling It is often the case that mathematical optimization is needed as a component of a larger simulation or to establish performance bounds on the results of a simulation. While in some cases the simulations required for the challenges cited in Chapter 2 may be too large or complex to optimize in any formal sense and the uncertainties may diminish the utility of optimization, in other cases optimization techniques can be quite useful. It is important to realize that such techniques are available when needed. Many “purposive” military systems are currently modeled as a collection of smart, and arguably rational, decision-making agents that attempt to continuously improve some overall objective function. Although this paradigm has its defenders and critics in the modeling community when used to model some nonmilitary systems, it has been recently shown that the paradigm of self-optimizing via agents can be used to optimize a variety of general large-scale complex systems (Ghate et al., 2005). These need not be systems of antagonistic elements, as is often assumed for military analysis. For example, distributed and/or decentralized control architectures have been studied in artificial intelligence and robotics (Bui et al., 1998). The typical setting involves a group of agents that have (more or less) homogeneous capabilities, share a common objective, and, critically, have access to an ad hoc protocol set for a large number of contingencies and for coordination among them-

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge selves. Since these protocols assume assured bilateral agent communication, determining the best protocols to use becomes computationally intractable when the number of contingencies and/or the number of agents increases. An alternative agent-optimizing approach, known as fictitious play (FP) (Brown, 1951; Robinson, 1951), addresses the challenge of optimizing black-box simulation models that cannot be expected to exhibit the kinds of regularity or convexity properties that conventional nonlinear optimization approaches demand (Bazaraa et al., 1993). The basic idea, inspired by game theory, is to animate the design components or controllable variables of a system by representing them as the decisions of intelligent, goal-seeking agents. These agents attempt to optimize their own selfish responses to an environment created by the behaviors of the other agents/components. This process can be viewed as an iterative game, with the components being players having identical interests—the overall performance of the system. Although in its infancy, this approach has been successfully applied to problems in the private sector (e.g., the joint optimization of plant-level production, capacity planning, and marketing decisions at one of the big three automakers) and the military (e.g., allocating resources and routing messages in mobile ad hoc networks and determining optimal ship-steering policies). For example, in typical traffic routing in a dynamic network, an individual vehicle has origin and destination locations, an origin departure time, and a finite set of sequences (routes) of road segments joining the origin with the destination. Each vehicle’s route traversal time is influenced by the traffic congestion on each link in the route during the time the vehicle is traveling along the link, so that the route traversal time depends on the choices of routes made by the other vehicles in the network. This system-optimal traffic assignment problem with flow-dependent costs has been studied extensively.13 However, a crucial distinguishing characteristic of this problem—namely, dynamic, time-dependent congestion on the links in the network—has rarely been considered. In this version of the problem, numerical procedures for evaluating transit times are simulation based (down to the individual vehicle) and require significant computational effort. The task of finding the system-optimal routes is therefore inherently complex, and it is the subject of a great deal of research in the fields of intelligent transportation systems and sequential dynamic systems.14 However, an FP algorithm has recently been proven successful in addressing this problem (Garcia et al., 2000; Lambert et al., 2005). There are several general computational advantages to FP when trying to analyze models of military complexity: It can be applied to complex optimization problems with a black-box objective function lacking special structure. It updates all decision variables independently and in parallel, making the approach scalable in the number of variables (unlike other global optimization approaches, such as simulated annealing). Convergence to an optimal solution is ensured in the limit (Ghate et al., 2005). In many applications, only one evaluation of the objective function is needed per iteration. In practice, very fast convergence to high-quality solutions is observed. Because of its general nature and its ability to optimize simulation-based models, FP warrants a thorough investigation. The committee has chosen to focus on FP as a particularly promising technique for optimization of highly complex, nonlinear systems. There are, of course, other promising approaches that merit investigation. The broader point is that serious investigation is needed into theories and methods for optimizing the performance of complex, adaptive networked systems. Other Methods for Representing Adaptive Systems The use of multiagent systems (MASs) allows developers an often appealingly intuitive and straightforward way of incrementally developing complex systems in a distributed and locally adaptive fashion. These are explored in the subsection after next. However, MASs are not the only way to model adaptive systems, and it is important that DoD continue to actively use, research, and develop other methods for modeling adaptive systems, as well as ways to compare the benefits and limitations of different modeling methods across different classes of problems. Different modeling methods are called for because of variations in the depth and fidelity required for a given application area and because of implementation issues, including efficiency, development time, and the expected operation of different asynchronous and autonomous segments of the system. Modeling methods that can represent adaptive behavior are needed because in many systems one cannot specify in advance all the conditions that could prevail and all the data that might be obtained. Furthermore, in many large, distributed real-time systems, no central decision-making element is fast enough to respond as needed to locally changing conditions. The word “agent” has been used for models, however implemented, that can generate solutions in an adaptive manner. Examples include genetic or evolutionary programming methods for solving optimization problems, and game-theoretic, control-theoretic, or rule-based methods for solving decision problems. However, in order to gain the advantages of different MAS methods and other methods for representing 13 See, for example, Potts and Oliver (1972). 14 See, for example, Patek et al. (2001).

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge adaptive behaviors, it is important to distinguish among the different desirable characteristics of adaptability exhibited by different implementation strategies and to identify the applications for which each is most desirable. It should be noted that these heuristic methods may be superseded in the future by new theoretical developments in the mathematics of optimization or by increased computer power. This report has not focused on techniques of deterministic optimization. When we talk about modeling methods that allow both local decision making and local individual history in a heterogeneous distributed environment, it is very natural to think in terms of agency. Agency can be implemented with any distributed, object-oriented modeling technique not just with those explicitly labeled “software agents.” The ability to adapt to specific, time-dependent inputs can also be implemented with decision rules, knowledge bases, and logic-based programming methods. In a well-characterized solution space, there are mathematical methods to combine or integrate local results, often described using sets of equations. Partial differential equation solvers and other synchronous computational methods are examples of important methods that may be usefully incorporated into an agent-based framework. That is, although equation solvers are sometimes viewed as antithetical to an agent-based solution, it is quite feasible to design an agent-based system in which some of the agents use inputs from their environment to construct equations that they then employ an equation-solver to solve and provide the results to other agents in the system. Similar remarks obviously apply to optimization, regression, and other methods often placed in opposition to agent-based systems. Currently deployed adaptive systems use a wide range of strategies for adaptation. Some—for example, control systems for complex electromechanical devices and systems such as UAVs—choose among existing models of the environment in response to results of measurements. The best of these allow for some online model building to help react appropriately in the short term to unexpected behaviors in the environment (usually combined with a call for human intervention). In addition, any amount of self-modeling that can be usefully interpreted allows the system to examine its own capabilities and plan its activities much more effectively, including identifying and reacting more quickly when problems occur and even determining in advance when problems might occur. Social Behavioral Networks A behavioral model is a model of human activity in which individual or group behaviors are derived from the psychological or social aspects of individuals. Much progress has been made in recent years in this area, and it is of central importance to many questions addressed by MS&A across the DIME space. There are a number of approaches to behavioral modeling. Among these, the key computational approaches that are important from a DoD perspective are social network models and multiagent systems. In this section, the committee briefly describes both approaches and their variants in order to motivate recommendations to improve their utility to DoD. More detail is found in Appendix B. Social network models represent relationships among individuals, the flow of information among individuals, and other aspects of the ways by which individuals are connected to and interact with each other. These models are based on graph theory, and because of that, traditional operations research flow and network models have been used for analysis. The nodes in such a model are individuals and the arcs are derived from relational data—that is, who knows whom, who works with whom, and so forth. There are three forms of analysis in this area: traditional social network analysis, link analysis, and dynamic network analysis. However, all of these forms of analysis, at their core, involve graph theoretic concepts and computations. Traditional social network analysis mainly involves statistical analysis to identify the topology of the network, the influential nodes, and the key positions in the network. Link analysis is concerned with pattern recognition in the network and used to look at the formation of cliques and other relationship groups. Dynamic network analysis adds simulation to traditional social network analysis and link analysis to look at network evolution. These three techniques are used to analyze relational data—that is, data about whether entities of one type relate to entities of another type. Successful analysis is heavily dependent on the existence of reliable data and the availability of computational resources. Social networks are a promising tool for studying many problems of importance to DoD, such as terrorist networks, the spread of infectious agents, flows of information and influence within enemy forces, and others. While considerable progress has been made in recent years, there are a number of weaknesses in the current state of technology for social behavior networks. A few of the most important limitations are these: Existing visualization techniques do not scale well, and interpretation of the results is dependent on the particular visual representation of the network. There is no agreed-on set of metrics for social behavioral networks, and those that do exist frequently do not correlate well with the property being measured. There are no standard techniques dealing with missing or erroneous data, and since these networks are data greedy, missing or erroneous data are a common problem. There is insufficient ability to link social networks to other events and locations, which is necessary to ensure that such networks are not used in isolation. In contrast to social network analysis, multiagent systems can be used to model the way in which social behavior emerges from the actions of a set of agents. Multiagent systems are computer-based simulations of a set of actors, called

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge Network Science The nascent study of networks per se, which would allow understanding them intrinsically rather than through particular instantiations, also shows promise as a foundation for advanced defense MS&A. Society depends on a diversity of complex networks, and this report has emphasized the growing dependence of the military on networks for information dissemination, command and control, and effects-based operations, among others. Despite this dependency, our fundamental knowledge about networks is in its infancy; indeed, there is no body of knowledge that can be called “network science.” A recent Army-sponsored report (NRC, 2006), referred to below as Network Science, is probably the first attempt to define both the need for and the substance of a science of networks. Although the report does not specify a rigid body of knowledge to be incorporated in the new field, it defines network science as consisting of the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena. Network Science identifies research areas of special interest to the Army that, in addition, apply more broadly to the entire DoD. One high-priority area is modeling, simulating, testing, and prototyping very large networks. Other aspects of networks that are relevant to MS&A include the impact of networked structures on organizational behavior (see the subsection “Social Behavioral Networks” in this chapter) and on enhanced networked-centric mission effectiveness (see the following subsection). In agreement with this report, Network Science concluded that advances in network science can address the threats of greatest importance to the nation’s security. Recommendation 8: DoD should support and extend initiatives to cooperate with other agencies funding research on networks. Building the Scientific Base for Embedded MS&A Present efforts to design and use complex, dynamic models are hindered by major gaps in the theoretical underpinnings of such models. For instance, the mathematical formalisms used in most modeling assume that the system being modeled is closed—that is, that the model output will always fall into a clearly defined space of possible outputs. But this assumption is violated for MS&A embedded in other systems, because the models must account for inputs from a dynamically changing set of sensors. The NSF has identified a number of advances needed in mathematics and statistics as part of its Dynamic Data-Driven Applications Systems (DDDAS) program, mentioned earlier in this chapter. For example, the DDDAS characteristic of allowing new data to be incorporated into running algorithms raises fundamental questions about the stability of those algorithms and their outputs. NSF’s program has developed fundamental analytical challenges for understanding and managing DDDASs: The creation of new mathematical algorithms with stable and robust convergence properties under perturbations induced by dynamic data inputs. Algorithmic stability under dynamic data injection/ streaming. Algorithmic tolerance to data perturbations. Multiple scales and model reduction. Enhanced asynchronous algorithms with stable convergence properties. Stochastic algorithms with provable convergence properties under dynamic data inputs. Handling data uncertainty in decision-making/optimization algorithms, especially where decisions can adapt to unfolding scenarios (data paths). Embedded MS&A systems must revisit a number of mathematical and statistical issues. These issues are given new prominence because the interaction between models and live data sources may cause small effects to cascade. These issues include Assessment and propagation of measurement error. Combining different types of uncertainties. Adapting to small sample sizes, incomplete data, and extreme events. Evaluation of quantization schemes. Optimization or satisficing within complex solution spaces. These issues are well known in the optimization community and have been extensively studied, but embedded systems face the additional challenge of adapting to the rapid and unpredictable changes resulting from new data or a new base model of the solution space. The use of MS&A in embedded situations creates a need for mathematical methods to enable the evaluation of partial or intermediate results. Embedded MS&A systems must be able to reason about how closely they have approached a good-enough solution in order to evaluate the trade-off between better results and the investment of additional sensing and computing resources. This requires measures of goodness that are meaningful in spite of uncertainty about the achievable end state, the means of dynamically adjusting the streams of input to move toward that end state, and the rates of convergence to some quality target. There might also be competing criteria of goodness. A step toward this capability would be the development of new analytic methods that could (1) characterize the solution space so that designers know something about its areas of sensitivity, boundaries, bad areas, and well-behaved areas and (2) characterize (using sensitivity analyses) the impacts of assumptions within the models or simulations that are running. Reasoning about

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge the progress of a given intermediate solution may also be thought of as an online process, where one is asking whether either more data or more time will yield a better solution. New mathematical models of computational processes might be helpful for this. In addition to the particular problems of embedded systems there are other areas where mathematical advances would find ready application: The use of structured random search and determination of problems amenable to such an approach. Scalability of mathematical methods, including better partitioning, abstraction, and aggregation methods. Methods for analyzing the results of model composition, interoperability, and resource integration, including methods for combining different formalisms, different definitions of uncertainty, and techniques such as metalogics, which allow one to reason about the characteristics of different logics and knowledge representations and their applicability to a specific problem. Methods for analyzing integrated modeling and data analysis environments. These would include research into the behavior of real-time linkage of models to data streams, perhaps using Bayesian methods to update model parameters with a combination of real and simulated data. Useful methods might also link machine-learning techniques for data extraction with simulation tools for forecasting. This report has identified embedded MS&A as an important component of the changing DoD landscape and as an area in need of additional scientific research. Recommendation 9: DoD should begin cooperative programs of research into embedded systems with other agencies facing similar demands. Expanded Concepts of Validation Some M&S is, or could be, solidly based in settled theory or empirical testing. Classical validation methods would then apply, and a model’s predictions could be compared against a trusted reference. In addition, successful analysis requires confidence in the model’s results, or at least an understanding of the limitations of the results. This said, many models and simulations, especially when considered to include the databases with which they will be used, contain a great deal of uncertainty (see Chapter 4 for an extensive discussion of this). If the data for previous events are known, at least retrospectively, then postdiction can be used to assess validity, but even that condition is often not met. The output of complex systems is influenced by one-time occurrences along the way that cannot be identified reliably even after the fact—when, for instance, a soldier is killed while on sentry duty or a surveillance aircraft is shot down without an impending or actual attack ever having been reported. Even worse, when dealing with complex systems, we often do not even know what the correct structure of a good model should be. We may have reasonable conjectures, but it is hardly unusual for experts to disagree fiercely on such matters. For instance, traditional validation methods might not be applicable to large-scale multiagent models used for examining sociocultural systems because the fundamental underlying laws either do not exist or are unknown. Considerations such as these necessitate a new concept of validation;16 it may be prudent to implement a means of labeling a model, simulation, or game as “valid for the purposes of exploration in a particular context.” This would be a judgment not about the truth of any one prediction but about whether, on balance, the tool was useful.17 Note that the important standard technique of judging face validity does not apply to the kinds of exploratory models and games on which this report focuses. Often the purpose of exploratory work is to uncover possibilities very different from what would usually be expected: system failures when certain odd combinations of events occur, such as long strings of (good or bad) luck; changes in the very structure of a social system due to personalities, deaths, random encounters at special times, and so on. If the model is used to find unexpected outputs, face validity is a poor judge of model validity. How might one assess validity, even for limited purposes of exploration? The committee is skeptical about the value of bureaucratic processes to assess validity, since they are expensive, time-consuming, and frequently reinforce conventional wisdom and standard databases even when the reality is massive uncertainty. Nonetheless, validity is an important matter. Several criteria are necessary to establish a model’s validity: The model should be sufficiently consistent with the laws of physics and realities of technology so that the insights apparently obtained are not artifacts of violations of these.18 The model (or game) should be comprehensible and explainable, often in a way conducive to explaining its workings with a credible and suitable “story,” thereby helping people to assess in real time whether an insight could be illegitimate or the result of artifact. Models used in exploratory analysis should deal rea- 16 Some M&S professionals feel that the term “evaluation” is preferable because it avoids the connotation, sometimes associated with “validation,” of a one-time process or step of bureaucratic certification. 17 Some of this discussion draws on Bigelow and Davis (2003). 18 This is not an idle example. It is not uncommon for “concept-driven” war games to assume that technology will provide whatever is necessary to achieve the concept. That can be a useful approach, but it can also be troublesome, as when a concept dependent on light, long-lifetime, powerful chemical batteries is embraced uncritically.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge sonably with all known classes of uncertainty, possibly deep uncertainty, and at least confront candidly the problem of unknown unknowns,19 with some combination of speculation or stating of assumption. Models should be falsifiable. As in science, assertions and predictions that are ultimately circular should not be tolerated. Multiresolution, multiperspective modeling can be very useful for validating troublesome models. One of the most convincing and economical ways to falsify some models is by looking at aggregate-level consequences and comparing them to aggregate-level empirical information. For example, if a detailed simulation shows complete military victory and successful stabilization with only a very small offensive force, then an excellent basis for skepticism is a low-resolution model reflecting historical experience that a much larger force had been deemed necessary in prior campaigns (Gordon and Trainor, 2006). Another approach to such validation efforts is to work methodically through the components of a model, such as one used for exploration, examining the validity of the mathematics and logic in each component and the presence of factors known empirically to be significant.20 Such testing is desirable when feasible, but the tester should be aware that because the modules of a complex system model are valid does not necessarily mean the overall system model is valid. Even models of complex adaptive systems can be given the capability to explain results (e.g., by instrumenting the model or saving all relevant data so that a step-by-step replay is possible), although the current state of the art for doing so is poor. If such explanatory capabilities are built in, then conclusions can be evaluated in part by the chain of events leading to a particular result. Of course, a flaw in model logic does not necessarily indicate that the insight was wrong. Nonetheless, this method can be quite powerful when available. Finally, sometimes a good way to assess model validity is to compare models and their “predictions” (even those of exploratory analysis) to the predictions of models built by other people, preferably with different mindsets. This is common in examining scientific disputes. The result may be to find important errors or omissions, to note significant differences without being able to evaluate relative correctness, or to find reasonable consistency—at least in a specific problem context. Games are even more problematic. Games are superb vehicles for revealing factors and considerations that might not otherwise be recognized and for building a “sense of the chessboard” and the moves that can be made. Some games also bring out a range of plausible and revealing human emotions, such as distrust and parochialism, and various misperceptions that are well-understood by cognitive psychologists. However, it seldom occurs to anyone who has played a game that the game should be validated. What would validation mean? Only one path through possibility space was traced out, and not everything happening in the game was necessarily realistic. Nonetheless, games might provide a new opportunity for the validation of social behavioral models—especially if they include cross-cultural players, which present a particularly difficult problem for verification. We have no way to monitor humans and their behaviors such that those behaviors could be provided as inputs to a social model and could produce an output—that is, an action or behavior that a human or group of humans might perform. However, massively multiplayer online games (MMOGs) provide an environment in which experimentation and testing might be performed. By some estimates the number of players participating in online games already amounts to 180,000 person-years of game playing.21 If these games could be instrumented and the behavior exhibited and captured, they could serve as virtual laboratories for the study of social phenomena. Recorded behaviors could then be tested against the outputs of social models. The difficulty, of course, is that current MMOGs are commercial; their mission is to provide an entertaining and engaging experience to customers/players, not to run experiments of interest to DoD. However, it might be possible for DoD to carefully negotiate the funding of a virtual laboratory that would attach to a commercial MMOG and that could be used by DoD to test its behavioral models and by the game owner as an analysis tool. The preceding discussion should be read not as suggesting a deemphasis on careful model evaluation but rather as urging recognition that “evaluation” must necessarily be quite different for models dealing with highly complex and uncertain phenomena than, for example, for engineering models. When dealing with issues that are less measurable but relevant to, say, effects-based operations, different methods are called for, and demands for validation in the classical sense are not pragmatic. INFRASTRUCTURE TO SUPPORT THE NEEDED MS&A CAPABILITIES The preceding section discussed capabilities needed for DoD’s MS&A in order to address the challenges of Chapter 2 19 Deep uncertainty is sometimes said to be uncertainty of the type one has when even the nature of the underlying processes is unknown. A statistician might refer to not knowing the nature of the probability distribution. The Secretary of Defense has referred to “unknown unknowns,” which are behaviors omitted from the modeling entirely because their existence is not recognized. 20 This is not always straightforward. In social science it is not uncommon for some experts to insist that a factor is important, even though there is no empirical basis. 21 Statistics available at http://www.gamasutra.com/gdc2005/features/20050309/postcard-diamante.htm.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge and some promising technical directions to pursue. In practice, DoD’s assessment of new MS&A capabilities will depend heavily on establishing a substantial forward-looking infrastructure. Laying the right infrastructure could have extraordinary benefits over the long run; failure to do so could greatly impede progress and efficiency. To build the capabilities described so far in this chapter, the committee regards the following issues as the most important with respect to infrastructure: Composability of M&S. The ability to improve efficiency and coherence by constructing higher-level models from lower-level components. Data collection and data farms. The ability to draw quickly on existing databases, whether of input assumptions or previously generated output. Visualization. The ability to visually interpret high-dimensional data. Chains of tools and computational platforms. The ability to used linked chains of tools and platforms. Service-oriented architectures. The ability to develop and use modularized functionality that is available on the network as a service. A definitive repository. The existence of a central virtual repository and clearinghouse for pointers and advice. Cooperation with other entities. The ability to communicate across organizational and cultural boundaries unhindered by stovepiping or bureaucratic or technical obstacles. This section explores these needs. Chapter 5 explores another important area of infrastructure, the educational background of the MS&A practitioners. Composability A recent technical review of model-composability issues (Davis and Anderson, 2004) discussed DoD model composability in some depth. The committee does not attempt to replicate the advice contained in that review except to highlight some of its main points and add commentary and recommendations. Appendix C provides more detail, including citations to the recent literature. Composability is the capability to select and assemble components in various combinations to satisfy specific user requirements meaningfully. In M&S, the components in question are themselves models and simulations. Composability implies the ability to assemble components readily in various ways for different purposes. It goes further than interoperability, which may be achieved only for a particular configuration, perhaps in an awkward one-time lash-up. To put it differently, composability is associated with modular building blocks. DoD’s experience with composability has been disappointing, despite the considerable priority accorded it and the promise it showed. As discussed in Davis and Anderson, four factors affect model composability: Complexity of the system being modeled. Difficulty in defining when composite M&S will be used. Strength of the underlying science and technology. Human considerations, such as the quality of management, the existence of a community of interest, and the skill and knowledge of the workforce. Davis and Anderson’s review recommended a number of priorities and actions, which are summarized tersely in Table 3.3. In addition to the priorities listed in the table, the committee offers the following general guidance on composability: To obtain the highest degree of composability, it must be engineered in. In general, DoD should treat composability as a matter of degree, measured as a function of the time and effort necessary and the flexibility obtained. By differentiating among (1) conceptual models, (2) implemented models, (3) simulators, and (4) experimental frames, the quality of MS&A can be substantially improved. There may be alternative ways to implement each of these, but without a clear distinction among them, it is hard to make sound judgments. DoD should continue to support the development of potentially standard ontologies, such as the Web Ontology Language (WOL), under development by the World Wide Web Consortium. These address key semantic and pragmatic issues important to the advancement of composability. Poorly documented legacy code will continue to be a challenge for composability into the indefinite future. DoD should invest in a selective program of retro-documentation. In a few high-leverage cases, DoD should reprogram legacy models that appear to be valuable but that are technologically obsolete or limited. Improved Data Collection for MS&A Models of complex systems usually require large amounts of data as inputs, for determining parameters and for validation. Collecting those data can become a technological challenge. DoD needs to automate, or semiautomate, the collection of data for building and validating new models and simulation systems. Key tools for this more automated approach will be improved data-mining and text-mining techniques. Data-mining and text-mining tools are becoming in-

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge TABLE 3.3 Recommended Priorities for Improving Model Composability Category Component Specific Priority Items Science and technology Military science for selected military domains Capabilities-based planning, effects-based operations, network-centric operations   Science and technology of M&S Model abstraction (including multiresolution modeling model families)     Validation     Heterogeneous M&S     Communication: documentation and new methods of transferring models     Exploration mechanisms     Intimate man-machine interactions   Standards Revisit standards, as in the pre-HLA days, but at the same time hurry to realign DoD’s direction with that of the commercial marketplace   Model representation, specification, and documentation Exploit commercial developments, especially for high-level specification Understanding   Develop methods to predict difficulty and cost of proposed composability projects     Commission independent lessons-learned study on experiences from JSIMS, JWARS, and one SAF Management   Define requirements and methods for developed first-rate M&S managers Workforce   Stimulate systematic education, selection, and training of M&S workforce, in cooperation with other agencies, academia, and industry General environment for DoD M&S   Improve incentives and mechanisms to improve industrial and other bases     Encourage marketplace of ideas and assure even playing field for competitions     As part of this, insist on transparency and exchange, reducing the scope of proprietary restrictions SOURCE: Adapted from Davis and Anderson (2004). creasingly important parts of the modern simulationist’s tool kit. Such tools pave the way for more automated collection of the large time-sensitive data sets that are now needed and will be even more needed for simulation, particularly in the DIME/PMESII areas. To date, however, data-mining and text-mining tools are limited in the following ways: Lack of automated or semiautomated ontology creators to facilitate data sharing and analysis in new areas. Limited ability to handle nontext data such as photographs. Limited ability to extract data from various formats such as pdf and PowerPoint. Lack of good entity extraction algorithms that automatically correct for typos, spelling errors, aliases, and the like. Absence of tools that can work with streaming data. For most MS&A of complex systems, there is a dearth of relevant data available in clean preprocessed form. Thus, to reduce the time spent by analysts on data collection and increase the time spent on analysis, automated and semiautomated tools for data gathering, cleaning, sharing, and other processing are needed. Such tools should include natural language-processing tools for extracting relational data from audio and text sources, Web-scraping tools, automatic ontology generators, and visual interpretation tools to extract network data from photographs and visual images. Appropriate subtools for node identification, entity extraction, and thesaurus creation are also needed. The development and availability of these tools in an interoperable environment is critical for providing masses of data that can be used for

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge model tuning and validation. More rapid data collection would also mean the availability of more data sets for doing the meta-analyses required for development of theoretical foundations for M&S. Finally, these tools are essential to providing the wealth of data needed by models of complex, adaptive systems if models are to accurately represent situations and organizations and be the basis for sound analysis. MS&A is turning to data-farming techniques to assess the response surface of a simulation.22 Data farming is a technique where thousands (and potentially millions) of simulation runs are done with the same tool but using different parameters for each run. The “farm,” or archive of data, is allowed to grow over time as the number of cases, even using related models and different data sources, grows. This results in a massive amount of data showing the behavior of a complex nonlinear model (the simulation) under a vast number of conditions. The output data are then automatically statistically analyzed and prepared for visualization, enabling the users to have a better understanding of system behavior. Insights are uncovered over time using sophisticated analysis tools and verified or disproved as the cases accumulate. This is critical for development and validation and for providing policy guidance. Such an approach is particularly valuable for system dynamic and multiagent MS&A efforts, where small changes in parameters can lead to large changes in outcomes. Because many models of complex systems require such large amounts of data and long run times, the space of results cannot be adequately mapped using traditional experimental procedures. This may be true even after exploratory analysis, which carefully chooses scenarios to evaluate. By placing the models in a data-farming environment, the number of virtual experiments considered, the space of possibilities examined, and the scope of conditions analyzed can be expanded, often by several orders of magnitude, thus providing a stronger basis for decision making. Further, once a model has been evaluated as appropriate for the intended application, the response surface equivalent can be used, where appropriate, as a rapid model in training situations. From the DoD perspective this approach is necessary if these models are to be used to provide actionable intelligence and support course-of-action (COA) analysis. While many models permit various COAs to be analyzed, the typical use of the models limits that analysis to between 10 and 30 or so different actions. To be sure, this is about an order of magnitude beyond what is done without the multiagent or system dynamic tools, where the number of COAs evaluated is typically fewer than 5.23 However, with data farming, two or three more orders of magnitude might be achieved. The point is not so much the number of cases run but, rather, the ability to understand better the potential consequences of attempting a particular COA in different circumstances. For systems sensitive to many details, recognizing and characterizing the classes of circumstances requires extensive computation. While the promise of data farming is great24 and the number and diversity of MS&A systems that can be placed in a data-farming environment is increasing daily, most MS&A systems have not been placed in such environments, for four principal reasons: The cost of gaining access to and using such systems, particularly in terms of personnel training and machine computation time, tends to exceed the resources typically allocated for development and analysis. There is little guidance available on the extent to which data farming is needed and on how to make use of such technology. Placing a simulation tool into a data-farming environment often requires substantial code development in the simulation and sometimes in the data-farming technology; although plug-and-play is the vision, the overall technology is still very primitive. The amount of simulated data that can be produced in this way is much greater than what can generally be stored, handled with modern databases, analyzed with current statistical tool kits, or meaningfully visualized with most visualization systems. Current data-farming environments have been used only on relatively simple models to analyze only a few outcomes and have not been linked to databases. Since data farming has such great potential for mapping the response surface of MS&A tools, the data-farming simulator must be able to reason about the trade-off between the precision of results and computation time and must implement those decision rules in the systems. Tools will be needed to reason about whether or not more time yields much better answers or enables a meaningfully wider sweep of the response surface. Tools will also be needed for automated data compression and visualization when the level of fidelity needed in the results requires a massive number of runs. (Note that these requirements overlap those identified earlier as needed for MS&A that will be incorporated in embedded systems.) Finally, one of the key limitations is computation speed. Multithreading, gridding, and parallelization all 22 If there is a theoretical basis for postulating an approximate structure to be tested and calibrated statistically, then the result may be a “motivated metamodel,” although in this case the purpose may be to summarize the implications of an ensemble of cases run with a relatively simple agent-based model rather than the behavior of a trusted detailed model. 23 This technology is directly relevant to exploratory analysis as discussed in the subsection “Exploratory Analysis.” As an example, suppose that a strategic/operational campaign model is to be used for exploratory analysis. If 10 parameters of the model are of particular consequence, then exploring the space of possible values with low, mid, and high values would mean 310 (more than 58,000) cases. Experimental-design techniques can reduce the cases, but the numbers are still very large, making the ability to use high-performance computing and data farms very attractive. 24 See, for example, Barry and Koehler (2004) and Sanchez et al. (2004).

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge reduce computation time, as do special-purpose integrated circuits for common nonlinear analyses. However, such techniques are still very much under development and not yet ready for the typical DoD user or for potential developers in most universities or small businesses. Hence additional efforts are needed to automatically transform code into multithreaded, grid-based, or parallelized versions, and chips that support rapid nonlinear processing need to be routinely embedded in the laptops and desktops used by developers. Visualization of High-Dimensional Data In general, commercial off-the-shelf visualization techniques are not yet available for high-dimensional data and dynamic data. Specific short-term military needs include the following: Ability to receive visual alerts to significant changes in data streams. Ability to overlay or gracefully move between social network and geospatial information. Ability to zoom through large networks (on the order of one million nodes) and drill down on information about individual nodes. Development of open-source visualization tools would speed the development and testing of other tools, because currently a great deal of effort is spent developing specialized tool kits for visualization. That is, most modelers have found that for their model to be accepted, they need good visualization. Consequently, the production of visualization tools for the interface sidetracks them from (and draws resources from) their primary goal of developing and testing new simulation tools. The result is a large number of suboptimal visualization tools designed for specific simulation engines. Common visualization tools are needed, just as we now have common statistical and database tools that can be employed by all simulation tools. Chains of Tools and of Computational Platforms To advance the functionality and applicability of multiagent systems (MASs) for defense purposes, the MAS and network analysis techniques must be integrated into tool chains—that is, linked analysis techniques that may include M&S along with other methods. An example of such a tool chain would be a pattern-discovery technique, used to derive equations from historical data, feeding into an MAS to evolve future systems. MAS techniques can be used to evaluate COAs and suggest areas for further data collection. Combining these techniques will enable new types of problems to be solved; for instance, combining social-network metrics with pattern-discovery techniques is key to building an understanding of how networks grow and evolve. This is not to suggest that DoD should move to large, integrated behavioral models—quite the contrary. What is needed is increased interoperability of the tools. MAS development frameworks and the explosion of network analysis tools is making social behavioral modeling much more widely available. Moreover, it is leading to the development of many small single-purpose tools. DoD should be taking advantage of this by encouraging interoperability. It is important to note that it would not be feasible to require all tools to be written in a single language or to use a single framework; rather, the integration of models from diverse domains and in diverse languages is needed. Multiple models, visualization tools, and related software should be available to address diverse problems, but in a way that data (real and virtual) can be easily shared among the various tools. There are a variety of things needed to support such interoperability. Standards for the interchange of relational data need to be developed. Behavioral modeling tools need to be Web-enabled, and XML-based input/output languages need to be developed. A uniform vocabulary for describing relational data needs to be developed; this is particularly critical as tools and metrics are coming out of at least 20 different scientific fields.25 For defense and intelligence applications, common platforms and data-sharing standards should be developed so that tools written in the unclassified realm can be rapidly moved, without complete redesign, to the classified realm. To enable interoperability, a common platform and common ontologies for these tools are needed. This, in turn, will allow novel problems to be addressed more rapidly by regrouping existing models. It will enable subject-matter experts to interact through their models, thereby facilitating a broader approach to problems and reducing the likelihood of a biased solution. Interoperability will also hasten rapid development and deployment. DoD operates in a dynamic and heterogeneous environment. Traditionally, the deployed forces have had minimal, if any, computational resources of note. However, as new systems are deployed, the warfighter will have access to computational resources at various levels. In some cases, such as some of the C4I and mission-planning devices, these are general-purpose computers. In others, they are embedded in the systems, which are moving to commodity processors. This is exemplified by the recent decision of the Future Combat System program to choose commodity processors rather than a traditional embedded processor. This shift to computational power available at all echelons and locations opens up new opportunities for the application of MS&A to support operational missions in real time. The availability of computational assets, linked via communication networks, will allow for the implementation not 25 These fields include anthropology, sociology, psychology, organization science, marketing, physics, electrical engineering, ecology, biology, bioinformatics, health services, forensics, artificial intelligence, robotics, computer science, mathematics, statistics, information systems, medicine, civil engineering, and communications.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge only of the concepts of NCOs but also of distributed M&S. This can be accomplished through some combination of two methods. The first is the use of the current data in the operational systems as the starting points for the simulations. The second is the use of increased connectivity to implement an echelon-based model for running simulations and doing analysis. The latter option is discussed here in more detail. While the concept of edge computing is not new, the ability of deployed forces to implement it is. In the edge-computing model, computational power is pushed to the most forward elements. At each level back to the institutional computational environments (most likely high-performance computing resources), there is a significant increase in the computational power available, as measured by both cycles and bandwidth. Thus, the ability to run large or computationally intensive simulations increases in proportion to the distance, in a network sense, from the front. Systems on the edge would tend to focus on computing time-critical information, while systems in the rear compute less time-critical but more computationally intensive elements of simulations. As an example, systems at the platoon level might compare ongoing actions to the plan, while systems in the rear are computing alternative COAs by doing simulations. When the operation deviates sufficiently from the original plan, the new plan has already been developed and is ready for dissemination. In many ways, this paradigm emulates the real process of command and control by having the execution element at the front edge concerned with the immediate and the element to the rear concerned more with the longer term. Complex MS&A for theater use must be compatible with, and take advantage of, this distribution of computation along a chain of platforms. If MS&A is to meet its potential, models of different but appropriate levels of fidelity must be operable at different levels of the chain of command and produce consistent results. In addition, these models must be effortlessly linked to other analytical tools and data sources, again appropriate to the level of the decision. Service-Oriented Architectures A key technology for addressing the interoperability and information sharing required by chains of tools and of platforms is the service-oriented architecture (SOA). In this context, the term “service-oriented” refers to customer service, not the branches of the armed services. An SOA mediates information exchange by means of services offered by service providers and used by service consumers (MacKenzie et al., 2005). Services are advertised and accessed by participants in a standardized way. Key issues for an SOA are visibility (the need for participants to see the resources offered by other participants), interaction (the processes by which those with needs and those with capabilities are matched with one another and can exchange information), and effect (the changes in the world that result from the interaction). A service is the mechanism by which needs are matched with capabilities and information exchange is effected. By itself, an SOA does not provide a solution to a domain problem. Rather, an SOA provides a means of organizing, composing, and delivering solutions and/or parts of solutions owned or controlled by various parties. Because the SOA concept is based on the market paradigm of autonomous agents exchanging items of value, it can be expected to scale more successfully than traditional top-down architecture concepts. In all but the simplest situations, meeting a consumer’s need typically requires invoking multiple capabilities offered by different sources and composing their outputs in some manner. To accomplish that, an SOA would have what is called a solution composition capability. A solution composition capability performs the following functions: Transform a need statement provided by the consumer into a problem description framed in terms of capabilities offered by service providers. Decompose the problem into subproblems that can be addressed by the available capabilities. Select appropriate capabilities to address the subproblems. Request services to exercise the needed capabilities. Receive outputs and combine them into a solution to the original need. Transform the solution into a form that can be accepted by the requestor. A fundamental requirement for a successful SOA is semantic interoperability between providers and consumers. Semantic interoperability requires more than just the ability to interchange a given type of data in a given format. Provider and consumer must attach the same meaning to the data being exchanged. Semantic interoperability is typically addressed through metadata and ontologies (Chandrasekaran et al., 1999). Metadata, or data about data, provide descriptive information about an entity of interest. While metadata have classically been used to represent structure within a single database, an SOA can use metadata to describe aspects of any resource, including access mechanisms, required policies, and provenance. Standardized metadata vocabularies with agreed-on semantics have evolved in many communities and can enable discovery and retrieval of relevant networked resources. An ontology is a formal representation of knowledge about a domain, typically expressed in a manner that can be processed by machines. Ontologies represent the types of entities that can exist in the domain, the properties these entities can have, the relationships they can have to one another, and the events and processes in which they can participate. However, current-generation ontology languages such as OWL have no means of representing uncertainty in ontologies. Because uncertainty is ubiquitous in DoD

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge MS&A, there is a need for ontology formalisms and languages capable of representing uncertainty associated with entities and their properties, interrelationships, and associated processes.26 Ontologies help to ensure that information is interpreted by consumers in the manner intended by the provider. Ontologies can also be used to reinterpret data. A simple example is unit conversion, as when a consumer specifies a need for an aircraft capable of transporting a given number of pounds and a provider specifies cargo capacity in kilograms. Ontologies would be referenced by the SOA to perform the appropriate compositions and transformations. Provider and consumer could each use their own units, and the service mediating the information exchange would be responsible for the conversion. Ontologies and metadata alone cannot solve the semantic interoperability problem. First, legacy systems might have idiosyncratic and nonstandard interface specifications that bear no obvious relationship to current standard metadata vocabularies and ontologies. Developing wrappers to translate between representations is typically labor-intensive and error-prone, and a fully faithful translation may not be possible. Second, an SOA needs to mediate between diverse communities of users, each with different needs, different knowledge, and different customized vocabularies. Any attempt to force common vocabularies and ontological commitments on participants is doomed to failure just as surely as would be an attempt to force all the world’s population to speak a single language. Third, even if it were possible to enforce a single common interface standard today, the standard would soon be out of date. New needs would inevitably arise that could not be met by the standard, and demand for change would escalate. Finally, populating metadata and developing ontologies is time-consuming and expensive. Thus, metadata and ontologies may be incomplete and out-of-date. The market paradigm underlying the SOA concept is instructive in this regard. Markets are ever changing, and successful products evolve with the changing market. A Definitive Repository The central repository for DoD’s M&S community, the Modeling and Simulation Resource Repository (MSRR), is maintained by the Defense Modeling and Simulation Office (DMSO).27 Rather than containing the actual data, it contains a series of user-entered records that describe each resource and where that resource is located. As the designated lead for DoD M&S, DMSO led the development of the MSRR as the clearinghouse for M&S resources across DoD. As a distributed system, there are 10 major nodes where the information is maintained. In addition to DMSO, each of the services has its own node,28 as do the Missile Defense Agency29 and the intelligence community.30 Special interest groups such as the Joint C4ISR Decision Support Center,31 the Object Model Resource Center,32 and the Modeling and Simulation Information Analysis Center,33 also have their nodes on the system. Collectively, the MSRR sites are supposed to encourage cost saving and cost avoidance by providing a framework within which DoD M&S activities can share resources. More of a card catalog than a repository, the nodes provide pointers to where the user can search and then follow up with the owners of the systems, data, and resources in order to obtain more information or the actual data. As can be inferred from this description, the MSRR reflects, and is limited by, the structure by which DoD’s M&S is managed. The real resources are distributed and available only to those who know their location and have the ability and personal relationships to acquire them. This is evidenced by the experience of one of the authors of this report. In order to acquire what amounted to an off-the-shelf database, it was necessary to make a personal call to a member of the engineering staff who had developed it (whose name was known only from viewing a demo), coordinating among three government agencies, and waiting three months for the paperwork to wend its way through the system. Access to models is often the same, or worse. While some of the models are programs of record, are formally maintained, and have a baseline and a defined distribution mechanism, quite a few are not. These latter models are often maintained by contractors who view them as their own intellectual property and are accessed on the basis of individual task orders to support specific events. Thus, there is no real configuration control board or development plan for them. As a result, it is often difficult to know exactly what is in a model or how something was implemented. This makes software reuse a difficult task, as the model that is available might not be the most current or, alternatively, the changes made during reuse might not be rolled over into the next version. The result is a large number of local baselines for the more common models. That there is no true centralized clearinghouse for M&S systems or data seriously hinders efficiency and reuse. With the problem compounded by many variations of models in common use, it is not surprising that M&S practitioners often feel it is easier to create or extend an in-house model or database than to reuse an existing one. 26 See, for example, Costa et al. (2003). 27 See http://www.msrr.dmso.mil. 28 See http://afmsrr.afams.af.mil; http://www.msrr.army.mil; http://nmso.navy.mil. 29 See http://bmdssc.jntf.osd.mil. 30 See http://umsrr.dmso/mil. 31 See http://extranet.itis.osd.mil/dsc/indes.shtml. 32 See http://omrc.msiac.dmso.mil. 33 See http://www.msiac.dmso.mil.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge Cooperation with Other Entities A final infrastructure issue is the collection of factors that limit cooperation between the DoD MS&A community and other federal agencies and nongovernmental institutions with relevant expertise. Of course, one of these factors is that DoD’s mission is perceived to be unique. This is certainly true for the warfighting mission, but DoD’s interest in DIME modeling and logistics modeling, for instance, has considerable overlap with interests found in the State Department and the Department of Homeland Security. Moreover, DoD could share its basic technologies and expertise with the many agencies that are seeking stronger capabilities in MS&A. One policy issue that limits cooperation with universities, and hence DoD’s ability to leverage more academic contributions to MS&A, is intellectual property (IP). For example, an examination of the University of California’s IP policy34 reveals that even if a company were to pay all direct and indirect costs of an R&D project (e.g., a project to develop new technology for M&S), it could at best obtain a royalty-bearing license for the life of any U.S. patent generated by the R&D, after paying an issue fee and a minimum annual amount. The same rules would apply for foreign patents, but only if the company agreed to reimburse the university for all costs involved in the patent filing and maintenance. A more common position is exemplified by the IP policy of the University of Georgia,35 which states that it is the university’s policy to retain rights and requires a license to exploit any invention or technology commercially. University employees have to assign the rights to their IP-based work to the university if university resources were used in any way. In industry, it is customary for prime contractors and subcontractors to jointly own the IP rights when they collaborate in technology development. On government contracts, the government is normally given “government rights,” which allows the contractor to retain the right to commercially market the product while the government can use it for its own purposes. The notable exception to this is the Small Business Innovation Research and Small Business Technology Transfer programs, where the companies retain the IP that they develop.36 These disparities in IP policies are a problem because they discourage industrial partners from funding academic research rather than doing it in-house or with a commercial entity. If the project is expected to generate any significant revenue, the royalty to the university would reduce the organization’s profit. Furthermore, the publish-or-perish mentality at most academic institutions will lead to disclosure of the innovation and could speed a competitor’s time to market. Because the potential long-term economic disadvantages often argue persuasively against working with academics, DoD may be losing the talents of some of the nation’s best scientists and engineers. The issues of IP, publication, and subject matter are not the only issues limiting interactions between system developers and academics. With the large number of foreign students in technical fields, export control regulations are a significant concern. Given the subject matter of most military simulations, International Traffic in Arms Regulations (ITAR) restrictions basically prevent any non-U.S. citizen from having access to the models without export agreements in place. This is a further disincentive to collaboration between academia and industry on operational systems. REFERENCES Barry, Philip, and Matthew Koehler. 2005. “Simulation in context: Using data farming for decision support.” Proceedings of the 2004 Winter Simulation Conference, Washington, D.C. Bazaraa, M.S., H.D. Sherali, and C.M. Shetty. 1993. Nonlinear Programming: Theory and Algorithms. New York, N.Y.: Wiley. Bigelow, James, and Paul K. Davis. 2003. Implications for Model Validation of Multiresolution Modeling. Santa Monica, Calif.: RAND. Brown, G.W. 1951. “Iterative solution of games by fictitious play.” Activity Analysis of Production and Allocation. New York, N.Y.: Wiley. Bui, H., S. Venkatesh, and D. Kieronska. 1998. “A framework for coordination and learning among teams of agents.” Lecture Notes in Computer Science 1441:164-178. Cares, J. 2006. Distributed Networked Operations. Newport, R.I.: Alidade Press. Carley, K.M. 2003. “Dynamic network analysis.” Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers. Washington, D.C.: The National Academies Press. Chandrasekaran, B., J.R. Josephson, and E. Benjamin. 1999. “What are ontologies and why do we need them?” IEEE Intelligent Systems 14(1). Costa, P.C.G., K.B. Laskey, K.J. Laskey, and M. Pool. 2005. Proceedings of the Workshop on Uncertainty Reasoning in the Semantic Web, International Semantic Web Conference, November. Available at http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS-Vol-173. Davis, Paul K. 2003. “Planning for adaptiveness.” New Challenges for Defense Planning: Rethinking How Much Is Enough. Santa Monica, Calif.: RAND. Davis, Paul K. 2006. “New paradigms and new challenges.” In M.E. Kuhl, N.M. Steiger, F.B. Armstrong, and J.A. Joines, eds., Proceedings of the 2005 Winter Simulation Conference. New York, N.Y.: Association for Computing Machinery. Davis, Paul K., and Robert Anderson. 2004. Improving DoD’s Model Composability. Santa Monica, Calif.: RAND. Davis, P.K., and J.H. Bigelow. 2003. Implications for Model Validation of Multiresolution, Multiperspective Modeling (MRMPM) and Exploratory Analysis. Santa Monica, Calif.: RAND. Davis, Paul K., Jonathan Kulick, and Michael Egner. 2005. Implications of Modern Decision Science for Military Decision Support Systems. Santa Monica, Calif.: RAND. Dupuy, Trevor. 1987. Understanding War: A History of the Theory of Combat. St. Paul, Minn.: Paragon House. Garcia, A., D. Reaume, and R.L. Smith. 2000. “Fictitious play for finding system optimal routings in dynamic traffic networks.” Transportation Research B, Methods 34(2). 34 See http://www.ucop.edu/raohome/cgmanual/chap11.html#11/340. 35 See http://www.ovpr.uga.edu/rpph/rph_chp2.html#Ownership%20of%20Intellectual. 36 See http://www.acq.osd.mil/sadbu/sbir.

OCR for page 17
Defense Modeling, Simulation, and Analysis: Meeting the Challenge Ghate, A.V., M.A. Epelman, and R.L. Smith. 2005. Sampled Fictitious Play for Complex Systems Optimization, Technical Report 05-15. Ann Arbor, Mich.: Department of Industrial and Operations Engineering, University of Michigan. Gordon, Michael, and Bernard Trainor. 2006. COBRA II: The Inside Story of the Invasion and Occupation of Iraq. New York, N.Y.: Pantheon. Holland, John. 1995. Hidden Order: How Adaptation Builds Complexity. Boston, Mass.: Addison-Wesley. Hughes, Wayne. 1989. Military Modeling. 2nd ed. Alexandria, Va.: Military Operations Research Society. Johnson, Stuart, Martin Libicki, and Greg Treverton, eds. 2003. New Challenges, New Tools for Defense Decisionmaking. Santa Monica, Calif.: RAND. Lambert, Robert, Steven Popper, and Steven Bankes. 2003. Shaping the Next One Hundred Years: New Methods of Quantitative, Long-Term Policy Analysis. Santa Monica, Calif.: RAND. Lambert, T.J., M.A. Epelman, and R.L. Smith. 2005. “A fictitious play approach to large scale optimization.” Operations Research 53(3). MacKenzie, C.M., K.J. Laskey, F. McCabe, P. Brown, and R. Metz. 2005. Reference Model for Service Oriented Architectures. Available at http://xml.coverpages.org/soa.html. Military Operations Research Society (MORS). 2004. Operations Analysis Support to Network Centric Operations. Minisymposium held on January 27-29. Nagel, K., M. Stretz, S. Leckey, R. Donnelly, and C.L. Barrett. 1997. TRANSIMS Flow Characteristics, LA-UR 973539. Los Alamos National Laboratory, Los Alamos, N.M. Nagel, K., R.J. Beckman, and C.L. Barrett. 1999. TRANSIMS for Urban Planning, LA-UR 984389. Los Alamos National Laboratory, Los Alamos, N.M. National Research Council (NRC). 2006. Network Science. Washington, D.C.: The National Academies Press. Patek, S.S., V. Venkateswaran, and J. Liebeherr. 2001. “Simple alternate routing and differentiated services networks.” Computer Networks 37(3-4):447-466. Potts, R.B., and R.M. Oliver. 1972. Flows in Transportation Networks. New York, N.Y.: Academic Press. Robinson, J. 1951. “An iterative method of solving a game.” Annals of Mathematics 54:298-301. Sanchez, S.M., T.W. Lucas, and T.M. Cioppa. 2004. “Military applications of agent-based simulations.” Proceedings of the Winter Simulation Conference, Washington, D.C. Sterman, John D. 2000. Business Dynamics: System Thinking and Modeling for a Complex World. New York, N.Y.: McGraw-Hill. U.S. Government Accountability Office (GAO). 2006. Defense Acquisitions: DoD Management Approach and Processes Not Well-Suited to Support Development of Global Information Grid. GAO-06-211. Washington, D.C. Waldrop, Mitchell. 1992. Complexity: The Emerging Science at the Edge of Order and Chaos. Simon and Schuster. Weigley, Russell. 1973. The American Way of War: A History of the United States Military Strategy and Policy. New York, N.Y.: Macmillan.