Handbook of Real-World Applications in Modeling and Simulation

By John A. Sokolowski and Catherine M. Banks

Introduces various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society

Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques.

Contributions from leading academics and researchers integrate modeling and simulation theories, methods, and data to analyze challenges that involve technological and social issues. The book begins with an introduction that explains why modeling and simulation is a reliable analysis assessment tool for complex systems problems. Subsequent chapters provide an orientation to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges across real-world applied domains. Additionally, the handbook:

• Provides a practical one-stop reference on modeling and simulation and contains an accessible introduction to key concepts and techniques

• Introduces, trains, and prepares readers from statistics, mathematics, engineering, computer science, economics, and business to use modeling and simulation in their studies and research

• Features case studies that are representative of fundamental areas of multidisciplinary studies and provides a concise look at the key concepts of modeling and simulation

• Contains a collection of original ideas on modeling and simulation to help academics and practitioners develop a multifunctional perspective

Self-contained chapters offer a comprehensive approach to explaining each respective domain and include sections that explore the related history, theory, modeling paradigms, and case studies. Key terms and techniques are clearly outlined, and exercise sets allow readers to test their comprehension of the presented material.

Handbook of Real-World Applications in Modeling and Simulation is an essential reference for academics and practitioners in the areas of operations research, business, management science, engineering, statistics, mathematics, and computer science. The handbook is also a suitable supplement for courses on modeling and simulation at the graduate level.

• Language: English
• Publisher: Wiley
• Release date: Mar 28, 2012
• ISBN: 9781118241264

Book preview

Contributors

Michel A. Audette, Ph.D., is Assistant Professor at Old Dominion's Department of Modeling, Simulation, and Visualization Engineering, where his research emphasizes patient-specific neurosurgery simulation, model-based surgical guidance, and surgical device development. Before coming to Old Dominion, he was R&D engineer at Kitware, as well as had postdoctoral experience at the Innovation Center Computer Assisted Surgery (ICCAS) in Leipzig, Germany, and at the National Institute for Advanced Industrial Science and Technology (AIST) in Tsukuba, Japan. He has broad expertise in medical image analysis and continuum mechanics, and has a highly collaborative approach to the simulation of surgical instruments and to anatomical modeling. He received his Ph.D. at McGill University, Montreal, Canada, where his thesis dealt with a laser range-sensing-based approach to the estimation of intrasurgical brain shift, and he helped introduce range sensing to the medical imaging community. He has patents in the United States and Japan.

Catherine M. Banks, Ph.D., is Research Associate Professor at the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University. Dr. Banks received her Ph.D. in International Studies at Old Dominion University in Norfolk, Virginia. She currently focuses her research on modeling states and their varied histories of revolution and insurgency, political economy and state volatility, and human behavior/human modeling with applications in the health sciences. Dr. Banks is the coeditor of Principles of Modeling and Simulation: A Multidisciplinary Approach, Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains (2010), and Modeling and Simulation for Medical and Health Sciences (2011) and is coauthor of Modeling and Simulation for Analyzing Global Events (2009), published by Wiley.

Joshua G. Behr, Ph.D., is Research Associate Professor at the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University and Professor within the School of Health Professions at Eastern Virginia Medical School. Dr. Behr received his training at the University of New Orleans, specializing in urban and minority politics. He has taught a variety of courses including public policy, GIS in public health, and modeling and simulation in healthcare administration. Currently, he is applying a systems science approach to the study of the impact of nonrecursive relationships among the structural environment, policy interventions, and choice of health venue on underserved populations with chronic conditions.

Andrey N. Chernikov, Ph.D., is a Research Assistant Professor in the Department of Computer Science at Old Dominion University. His research interests include image analysis in medical and material modeling and simulation, parallel computational geometry with a focus on quality mesh generation, parallel and multicore scientific computing, and hardware–software interface. Dr. Chernikov received his Ph.D. in Computer Science from the College of William and Mary in 2007 with a Distinguished Dissertation Award. After his doctoral studies, he held Visiting Assistant Professor and Postdoctoral appointments at William and Mary.

Nikos P. Chrisochoides, Ph.D., is the Richard T. Cheng Professor of Computer Science at Old Dominion University and John Simon Guggenheim Fellow (2007) in Medicine and Health. His research interests are in medical image computing and parallel and distributed scientific computing, specifically in real-time nonrigid registration, image-to-mesh conversion, parallel mesh generation, both theoretical and implementation aspects. Dr. Chrisochoides received his BSc in Mathematics from Aristotle University, Greece, and his MSc (in Mathematics) and Ph.D. (in Computer Science) degrees from Purdue University. Then he moved to Northeast Parallel Architectures Center (NPAC) at Syracuse University as the Alex Nason Postdoctoral Fellow in Computational Sciences. After NPAC, he worked in the Advanced Computing Research Institute, at Cornell University. He joined (as an Assistant Professor in January 1997) the Department of Computer Science and Engineering at the University of Notre Dame. In the fall of 2000, he moved to the College of William and Mary as an Associate Professor, and in 2004 he was awarded the Alumni Memorial Distinguished Professorship. Dr. Chrisochoides has more than 150 technical publications in parallel scientific computing. He has held visiting positions at Harvard Medical School (spring 2005), MIT (spring 2005), Brown (fall 2004 as IBM Professor), and NASA/Langley (summer 1994).

Andrew J. Collins, Ph.D., is a Research Assistant Professor at VMASC, where he applies his expertise of game theory and agent-based modeling and simulation to a variety of projects including foreclosure and entrepreneur modeling. Dr. Collins has spent the last 10 years, while conducting his Ph.D. and as an analyst for the United Kingdom's Ministry of Defence, applying Operations Research to a variety of practical operational research problems.

Christine S. M. Currie, Ph.D., is a lecturer of Operational Research in the School of Mathematics in the University of Southampton, where she also obtained her Ph.D. She is now Managing Editor for the Journal of Simulation and previously the Book Review editor. Christine has been cochair of the Simulation Special Interest Group in the UK Operational Research Society for a number of years and involved in the organization of the UK Simulation Workshop. Her research interests include mathematical modeling of epidemics, Bayesian statistics, revenue management, variance reduction methods, and optimization of simulation models.

Saikou Y. Diallo, Ph.D., is Research Assistant Professor at the Virginia Modeling Analysis and Simulation Center (VMASC) of the Old Dominion University (ODU) in Norfolk, Virginia. He received his MS and Ph.D. in Modeling & Simulation from ODU and currently leads the Interoperability Laboratory at VMASC. His research focus is on command and control to simulation interoperability, formal theories of M&S, web services, and model-based data engineering. He participates in a number of M&S-related organizations and conferences and is currently the cochair of the Coalition Battle Management Language drafting group in the Simulation Interoperability Standards Organization.

Rafael Diaz, Ph.D., is Research Assistant Professor of Modeling and Simulation at Old Dominion University's Virginia Modeling, Analysis, and Simulation Center (VMASC). He holds an MBA degree in financial analysis and information technology from Old Dominion University and a BS in Industrial Engineering from Jose Maria Vargas University, Venezuela. He has published on a wide range of topics: simulation-based methodology, times series methodology, production of service and manufacturing systems, production economics, public health, and emergency department utilization. His research interests include operations research, operations management, logistics, healthcare systems, reverse logistics, dependence modeling for stochastic simulation, system dynamics, and simulation-based optimization methods. He worked for six years as a process engineer and management consultant before his academic career.

Barry C. Ezell, Ph.D., is Research Associate Professor at VMASC where he leads the homeland security and military defense applied research area. His most recent sponsored research includes US Department of Homeland Security in bioterrorism risk assessment and adaptive adversary modeling and Virginia's Office of Commonwealth Preparedness for Hampton Roads Full Scale Exercise. He serves as associate editor for Military Operations Research (MOR), editorial board member for the International Journal of Critical Infrastructures Systems (IJCIS), and Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science. Dr. Ezell is a member of the Society for Risk Analysis, Military Operations Research Society, and Association of the United States Army and a recipient of the Society for Risk Analysis' Best Paper in a Series, 2010.

José J. Padilla, Ph.D., is Research Scientist with the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University, Suffolk, Viginia. He received his Ph.D. in Engineering Management from Old Dominion University. He holds a BSc in Industrial Engineering from la Universidad Nacional de Colombia, Medellín, Colombia, and a Master of Business Administration from Lynn University, Boca Raton, Florida. Dr. Padilla is part of the M&S Interoperability group at VMASC. His research interest is on the nature of the processes of understanding and interoperability and their implications in the study of Human Social Culture Behavior (HSCB) modeling.

R. Michael Robinson, Ph.D., is Research Assistant Professor at the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University, where he leads the Transportation Applied Research team. Past research has been sponsored by the Virginia General Assembly, Virginia Departments of Transportation and Emergency Management, and the US Department of Transportation. His research focuses on transportation planning and operations, especially during emergency conditions, and includes the influence of human decision making.

John A. Sokolowski, Ph.D., is Executive Director of the Virginia Modeling, Analysis, and Simulation Center (VMASC) of Old Dominion University. VMASC is a multidisciplinary research center of Old Dominion University. VMASC supports the University's Modeling & Simulation (M&S) degree programs, offering M&S Bachelors, Masters, and Ph.D. degrees to students across the Colleges of Engineering and Technology, Sciences, Education, and Business. Working with more than one hundred industry, government, and academic members, VMASC furthers the development and applications of modeling, simulation, and visualization as enterprise decision-making tools to promote economic, business, and academic development.

Mandar Tulpule, is currently pursuing a Ph.D. in Modeling and Simulation at the Old Dominion University's Virginia Modeling, Analysis, and Simulation Center (VMASC). He holds an ME degree in Industrial and Systems Engineering from the North Carolina State University, Raleigh, and a BE in Mechanical Engineering from Pune University, India. His key research interests include modeling & simulation, operations management, supply chain, and logistics. He has experience as a manufacturing and supply chain engineer before his academic career.

Preface

Modeling and simulation is an important tool for representing or characterizing, understanding or analyzing, assessing or solving real-world problems. These dilemmas are unapologetically diverse, ranging from simple traffic jams to terrorist communications infrastructure; they differ in complexity from simple distribution chain adjustments to predicting human decision making. As such, these problems require a variety of methods to evaluate the phenomena and to proffer a solution. Modeling and simulation facilitates that need. Within the M&S toolbox is a variety of methods to represent (model) and iterate (simulation) entities and phenomena across numerous domains or applications. This handbook provides an orientation to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society. The handbook delves into six real-world applied domains: transportation, risk management, operations research, business process modeling, medical, and military interoperability.

Our approach is to introduce the handbook with a discussion of why M&S is a reliable analysis assessment tool for complex systems problems (Chapter 1). We will then introduce Human Behavior Modeling, the means to characterize decision making and the factors that shape and affect those decisions (Chapter 2). This type of modeling is often integral to the modeling conducted among other M&S domains. Moreover, to develop representative real-world models, components of human behavior modeling are necessary to accurately characterize the system and its simulations. The next six chapters are individual discussions of real-world applications: Transportation, Homeland Security Risk Modeling, Operations Research, Business Process Modeling, Medical, and Military (Chapters 3–8).

To M&S professionals, practitioners, and students who will be reading this text, we offer a concise look at the key concepts of modeling and simulation to include theory, development, execution, and analysis. Case studies are found in each chapter. They serve to introduce a methodology for the research and development of a model, assess human behavior, and demonstrate real-world applications.

While figures in the book are not printed in color, some chapters have figures that are described using color. The color representations of these figures may be downloaded from the following site: ftp://ftp.wiley.com/ public/sci_tech_med/modeling_simulation.

John A. Sokolowski

Catherine M. Banks

Introduction

Contemplating a National Strategy for Modeling and Simulation

At the close of the July 2010 Modeling and Simulation Stakeholders Meeting held in Washington, DC, a consensus was held among the 41 attendees: a national strategy is needed to advance the nation's newest technology, modeling and simulation (M&S). Called together by the Congressional M&S Caucus (headed by Virginia Representative Randy Forbes), this group was tasked with contemplating steps to a continued dialogue that would lead to a collaborative, cooperative focused National Plan. In essence, how does the government fully exploit, fully engage, and continue to develop this new capability at the national level?

As an M&S educator and researcher, I am compelled to respond to this task. As such, throughout this paper, I proffer my opinion and/or assertion as private views that are not to be construed as official, or as reflecting the views of the Old Dominion University. To begin this assignment, I thought it helpful to review how and why we have arrived at this national juncture. Briefly reflecting on the answers to these and other questions lends itself to proffering suggestions/recommendations for developing an M&S national strategy. This paper presents a succinct discussion of why M&S deserves national attention, where that attention should focus, and how those in the M&S community can support a national strategy to do just that.

A National Strategy?

Have you ever wondered what constitutes a national strategy, or who can call a national strategy? What event or phenomena or entity can claim that degree of attention? One dictionary tells us that a national strategy combines the art and science of developing and using the diplomatic, economic, and informational powers of a nation… to secure national objectives. A good example of that definition in action is the Eisenhower Administration (1953–1961) and its institution of a full-scale effort to advancing aeronautics and the military–industrial complex. The President did this on October 1, 1958, when he placed the National Aeronautics and Space Administration (NASA) under the Executive branch and provided it with an annual budget of $100 million along with three major research laboratories and two small test facilities. Today, that $100 million would equate to the buying power of $760,383,802.82 (with an annual inflation over this period of 3.98%). What was the impetus for this national attention, this national strategy in support of NASA? One could safely say the Soviet space program and the world's first artificial satellite, Sputnik 1. This accomplishment by the Soviets alarmed the Congress—the Soviet program was a perceived threat to national security and technological leadership. That threat no longer exists; however, the benefits of the research and development that took place during the heyday of NASA are immeasurable. The United States had another period of fast-paced technological growth that also had a national effect, but no real national strategy.

This age started when integrated circuit technology and microprocessors decreased the size and cost of computers while providing increased speed and reliability. The rest is real-time computer history that places computer diversity and capability in a state of perpetual transformation. But the United States was not the only actor in this computer phenomenon. The Japanese economy revolved around the computer industry; this, coupled with major successes in automobile development, placed that economy front and center for nearly two decades. In a 1995 essay, Comparative Study of the Computer Industry of Japan and the US, Caitlin Howell (University of Wisconsin, Department of Computer Science) contrasted the computer industries in Japan and America. This study drew attention to the relevance of the computer industry to the information revolution and how that technology and revolution transformed the economy at the national and global levels.

After reading this essay I wondered, did Computer Science, the discipline that serves to train professionals in the development of computers as tools, get a national strategy? If not, why? Do a quick Google search, and you will note a number of expositions discussing the need for a National Strategy in Computer Science, but those discussions are more indirect, focusing on protecting cyberspace identity, securing future technologies, and building digital preservation. If any national strategies that focused on preserving and advancing US technological capacity were put into place, they are not apparent. Rather, focused discussions, such as those listed above, as well as topics revolving around computer technology in the classroom and STEM (science, technology, engineering, and mathematics) coursework concerns abound. As such, I came to a few conclusions as to why no straightforward strategy was put into place for computer science:

1. It was viewed predominantly as a technology; therefore, it was quickly integrated into the international and commercial arena; thus, no national strategy could get its arms around it

2. As a technology, it saturated all elements of society—from the white collar professional to the geek to the small businessman with his need for software applications such as Excel and Quicken.

I also considered the evolution of product output (from desktops to notepads) and the outreach of the telecommunications infrastructure. I concluded that no one entity can grab hold of this unbounded technological whirlwind that has been a part of this postmodern world for nearly three decades. So, is it feasible to think that a national strategy should be implemented for M&S? And if so, should consideration be given to M&S as a discipline as well as a technology (or tool)?

Why M&S Warrants a National Strategy

As an academician I do not separate the discipline of M&S from technology, as both coexist and are codependent. With that stated, I take the position that both the discipline—its coursework, research, and development—and the technology warrant a national strategy of oversight and support. I am pleased to note the growing endorsement of Congress (House Bill 487) and its recognition of M&S as a critical technology. With that, I proffer two fundamental reasons why M&S research, development, and technology warrants a national strategy.

The first is from a global perspective in that M&S is vital to national security relative to military and homeland security issues. The United States needs this technology to ensure remaining at parity (an expression straight out of the Eisenhower playbook) or regaining the technological lead with countries that are striving to achieve technological dominance. Unlike its experience with computer science, the US cannot gamble on allowing market forces shape the outcome of M&S technology—a national strategy is needed to coordinate M&S development in the international and commercial arenas.

The second reason stems from a need to solve domestic problems. The US homeland faces challenges in a number of domains. Government and industry must provide solutions and take proactive measures in healthcare, transportation (to include infrastructure), and energy alternatives—these challenges must be examined. M&S is the only technology that can model, test (with repeated retesting if needed), analyze, and proffer solutions to these and other decision-making challenges because it has at its disposal a variety of means. And this is what makes M&S significant.

M&S, A Synthesis of Approaches (Techniques) and Paradigms

M&S has at its disposal various modeling techniques, such as complex systems modeling and holistic modeling, which can engage and integrate different modeling paradigms. This capability allows for a better representation of an entity or system being modeled and a better characterization of what-if scenarios as played out in simulation.

Systems-based approach to modeling refers to system theories, philosophies, and models as well as the concepts and constructs that are building blocks of those theories, philosophies, and models. It engages the science and technology of understanding in observing interactions among people and things (events or machines) on the simple premise that man is a complex system—when he interacts with another man or another system (thing or event), the result is an even more complex system. Theoretically, all these subsystems must perform in a certain manner for the entire system to function.

Holistic modeling includes undertaking comprehensive representations such as those found in human behavior (traditionally associated with the social sciences) and human modeling (such as modeling done in the medical and health sciences). Scholars in these disciplines continue to make use of various modeling tools to attain accurate characterizations of phenomena and historic events as well as representation of the human anatomy and human response.

M&S has become a recognized tool for exploring real-world phenomena (events) or as engineers would describe it, systems. This is especially true with phenomena or systems that cannot be readily manipulated for experimentation purposes such as systems that include human behavior and social networks. The challenge is to develop a computational representation of these systems in a verifiable and validated manner. Importantly, the computational representation must be able to capture soft data, as omission of this data would detract from model accuracy. Significantly, M&S scholars have developed a means to do just that.

Regarding modeling in the medical and health sciences, M&S now possesses a variety of modeling tools that can represent many aspects of life, including life itself. M&S is providing practitioners in these fields the capability to better understand some of the fundamental aspects of healthcare such as human behavior, human systems, medical treatment, and disease proliferation. This is done by engaging the three modes of M&S (live, constructive, and virtual) through simulations developed from computational and physical models.

Some of the modeling paradigms used in both complex systems-based approach and holistic modeling include:

System Dynamics modeling—which deals with the simulation of interactions between objects in dynamic systems

Game Theory modeling—is tied closely to the problem of rational decision making

Agent-based modeling—serve to imitate the actions and interactions among units of analysis or agents (representing people, organizations, countries, entities—any type of social actor), and the sequence of actions and interactions of the agents over a period of time

Social Network modeling—focusing on social behavior as it takes into account relationships derived from statistical analysis of relational data

Both systems-based and holistic modeling techniques expand the analysis of physical models with the integration of qualitative analysis that addresses social and political aspects of emergency management. Both techniques facilitate mixed-methods research: coupling quantitative data, qualitative analysis, hypothesis, and multiple testing of hypothesis (via simulation). In addition, M&S is arguably the only method that will allow for scientific investigation of multiactor, multivariable case studies to make possible understanding how a system is responding as a whole.

Over 10 years have passed since the Institute of Industrial Engineers (IIE) codified the advantages of using modeling and simulation. Their early assessment made a strong case for applying M&S to research and training. All of what they noted then still applies today:

choose correctly by testing every aspect of a proposed change without committing additional resources

compress and expand time to allow the user to speed up or slow down behavior or phenomena to facilitate in-depth research

understand why by reconstructing the scenario and examining the scenario closely by controlling the system

explore possibilities in the context of policies, operating procedures, and methods without disrupting the actual or real system

diagnose problems by understanding the interaction among variables that comprise complex systems

identify constraints by reviewing delays on process, information, materials to ascertain whether or not the constraint is the effect or cause

develop understanding by observing how a system operates rather than predictions about how it will operate

visualize the plan with the use of animation to observe the system or organization actually operating

build consensus for an objective opinion because M&S can avoid inferences

prepare for change by answering the what if in the design or modification of the system

invest wisely because a simulated study costs much less than the cost of changing or modifying a system

train better in a less expensive way and with less disruption than on-the-job training

specify requirements for a system design that can be modified to reach the desired goal

A few other facts about M&S bear mentioning. The discipline itself and the tools of the discipline are growing at a FASTER PACE than did its predecessor, computer science. An expanding cyclical advancement is taking place because of the advances in the technology M&S uses and because M&S serves to advance technology. In addition, M&S warrants national attention as it encompasses an INTEGRATED FACE because it incorporates various techniques and paradigms, which are then engaged across the disciplines making M&S truly multidisciplinary. M&S is also proving a BROADER BASE. M&S as a training tool can be found in user domains across the workforce (professional and nonprofessional) and in all learning environments. With these things in mind, I am compelled to call for a coordinated, national effort that will oversee this critical technology.

A Proposed Strategy

As an M&S stakeholder, I am to consider the following questions put forth at the July meeting as a way of shaping a national strategy. Here are my comments and recommendations.

Q 1. What are the impediments to underscoring US industry's role in order to promote expanded application of M&S technologies across the domains?

I see two major impediments, a formal recognition of the M&S industry and the M&S discipline. As such, I propose the following: (i) Establishing M&S as a legitimate and recognized industry in the United States. This would mean garnering renewed support to overcome the previously denied granting of an industry classification by the North American Industry Classification System (NAICS). A recognized M&S industry code would facilitate monitoring of the scope of M&S activity in the country, which can be measured and tracked by the Department of Labor. (ii) Recognizing modeling and simulation as an academic discipline with its own body of knowledge. This will provide students with the assurance that they can pursue M&S as a profession and as a career. It will also help develop a cadre of professionals who are formally trained in the core aspects of M&S, which will produce better M&S technology and solutions in the long term.

Q 2. What national goals/initiatives are already in place to support the acceptance and viability of widespread use of M&S across industries? How can these be better integrated?

At present, there exists an M&S Caucus at the federal level of government (the US House of Representatives), designed to support and encourage M&S technology. I would encourage or perhaps require a larger membership in the Caucus to facilitate a widespread recognition and discussion across the country. Also needed is an M&S Caucus in the US Senate.

Q 3. Is a national plan of action required in order to provide enhanced coordination and cooperation between regions? If so, what shape might it take?

Yes, a national plan or national strategy is needed. This can be approached from the research and development perspective via the establishment of a formal M&S research agenda for the nation. This item makes certain that we address critical M&S technological issues that will benefit both the core growth of M&S and add to the enhancement of modeling and simulation's ability to address ever increasing complex problems. The Department of Education can play a large role in this as well as other national institutions such as the National Science Foundation and the National Institutes for Health. The focus can be on domains of M&S implementation: homeland security, transportation and infrastructure, energy.

Q 4. What business and organizational models might there be to inform further work on instituting a consolidated, collaborative action plan?

I recommend establishing an office in the executive branch of the government to oversee and coordinate this national strategy. This office will ensure a continuing coordinated effort among several agencies at the national level such as the National Science Foundation, the National Institutes of Health, and technology stakeholders such as Department of Defense, Department of Homeland Security, and Department of Education, to name just a few. A good model to follow would be the one mentioned at the outset of this discussion—NASA.

So, is it feasible to think that a national strategy could be implemented for M&S? And if so, what considerations should be given to M&S as a discipline, as a technology, and as a tool? Yes, a strategy can and should be implemented by first recognizing with heightened significance the importance of this critical technology and its role in securing the nation globally and domestically. Due consideration must be given every component of M&S from the teaching, research, and development that takes place in the classroom to the application of analysis derived from repeated testing and simulation that only M&S can facilitate to the provision of services and treatments via M&S tools. It is my intent that this discussion makes clear that it is not only possible to execute a national strategy but also necessary.

John A. Sokolowski, Ph.D.

Executive Director, Virginia Modeling, Analysis and Simulation Center

Associate Professor, Department of Modeling,

Simulation and Visualization Engineering, Old Dominion University

Chair, Governor's Advisory Council on Modeling and Simulation

Member, Board of Directors, Society for Modeling and Simulation International

Member, Board of Directors, National Modeling and Simulation Coalition

Chapter One

Research and Analysis for Real-World Applications

Catherine M. Banks

1.1 Introduction and Learning Objectives

Modeling and simulation (M&S) has made a name for itself as a discipline with its own body of knowledge, theory, and research methodology and as a tool for analysis and assessment. Significantly, M&S has attained this broad and meaningful position in a few short decades paralleling the technological advances of mainframe and desktop computers, the ever-expanding internet, and the omnipresent digital communications infrastructure. In 1999, the National Science Foundation (NSF) declared simulation the third branch of science (1). In a 2006 NSF report entitled, Simulation-Based Engineering Science: Revolutionizing Engineering Science through Simulation, a focused discussion ensued on the challenges facing the United States as a technological world leader. The report proffered four recommendations to ensure U.S. maintenance of a leadership role in M&S as a strategically critical technology. Foremost was the call for the NSF to underwrite an effort to explore the possibility of initiating a sweeping overhaul of our engineering educational system to reflect the multidisciplinary nature of modern engineering and to help students acquire the necessary M&S skills (2). As noted in the Introduction of this text, a national movement is underway to ensure the role of M&S as a future technology. M&S education is a must for anyone who desires to be a part of that future technology. And it begins with acquiring an understanding of the four precepts on which M&S is premised: modeling, simulation, analysis, and visualization:

Modeling or creating an approximation of an event or a system.

Simulation or the modification of the model in which the simulation allows for repeated observation of the model as well as the methodology, development, verification and validation, and design of experiments.¹

Visualization or the representation of data and the interface for the model as appropriate for conducting digital computer simulations providing an overview of interactive, real-time 3D computer graphics, and visual simulations using high level development tools.

Analysis of the findings or simulation output to draw conclusions, verify, and validate the research to make recommendations based on various simulations of the model as well as the inclusion of the constraints and requirements of engaging M&S as a way of declaring the limitations of the research.

Technological advancements have paved the way for new approaches to modeling, simulation, and visualization. Modeling now encompasses high degrees of complexity and holistic methods of data representation. Various levels of simulation capability allow for improved outputs and analysis of discrete and continuous events. State-of-the-art visualization allows for graphics that can represent details so intricate as to be found within a single shaft of hair (3). Once the domain of the engineering and computer science disciplines, M&S is now accepted as a multidisciplinary field of study capable of an expanding body of knowledge and user-friendly applications to address any research that calls for integrating quantitative and qualitative research methods and diverse modeling paradigms. M&S has moved far from static modeling; it is capable of representing the animate and the inanimate, and intangibles such as aspects of life, as well as life (human modeling) itself. Thus, M&S serves as a means of analyzing, assessing data to provide information for decision making, and/or teaching and training.

1.1.1 Learning Objectives

This chapter presents a broad look at M&S for research and analysis beginning with

a contemporary look at M&S and its applied domains—background;

a discussion of the theoretical foundation—M&S theory and toolbox;

overview of research methods—research and analysis methodologies;

case study—engaging a research methodology to analyze a problem;

opportunities to test your understanding—exercises.

The primary learning objective of this chapter is for the reader to appreciate the breadth of opportunity M&S presents as a research and analysis tool. Inherent in the process of modeling is the required in-depth research of the event or system being modeled. This is because models are driven by data and so the data collection must be done with great accuracy. It can be said that a model is only as good as the data used to develop it. Specific to analysis is simulation development and the outputs that facilitate a variety of opportunities to review—also known as analyze—the intent of the modeling effort such as the analysis of a research question. And this is the case because M&S allows for a retesting of the hypothesis by allowing for iterations of the model's inputs. Thus, analysis can include determining attributes or time-sensitive changes to answer questions of a more predictive nature. For example, a model can replicate a protest scene with data representing protester attrition due to fear of arrest or fear of being accosted by counter-protest law enforcement (police). The common sense conclusion is that the police will eventually bring an end to the incident. But when? And how many policemen are needed to do this? What is the ratio of protestor to police needed to quash the protest? There are other factors that the model must represent: attributes of the environment, intent of the protest, the nature of the leadership, and the overall attitude of the protesters (pacifist or violent). These data inputs, and various iterations of the inputs via simulations, allow for potential outcomes or predictive assessments of the situation. Only M&S has the capability to redraw and retest the model and research question to provide specifics as to ratios of protesters to police or tipping points for change.

The secondary learning objective is to have a comprehensive grasp of the background, theory, paradigms, and domains (applications) of M&S. Putting these pieces together affords the M&S professional a holistic approach to the developer—user aspects of M&S. And importantly, it ensures the fundamental M&S protocols of verification and validation: Did we build the right thing (as to function and purpose)? Did we build it right (as to degree of correctness)?

1.2 Background

When did M&S make its first appearance? Is it a new field of study coupling engineering and computer science knowledge and skills, thus making it a cog in the wheel of technology evolution? Indeed, as a stand-alone discipline it is relatively young, but as a tool to examine, explore, and train it has existed for centuries offering much more than engineering and predating computer science. In fact, one can reasonably argue that the origins of modeling began in the ancient world in the form of live training as conducted by the Roman armies from c.500 bce–1500 ce.

This was followed by an age of sophisticated art and complex architecture, c.1200–1600. Artists of the Renaissance made use of modeling as a means of conceptualizing their designs before beginning a project. One of the most ardent users of modeling was Leonardo da Vinci. His collection of work includes paintings, sculptures, building designs, advanced weaponry, flying machines, and anatomical studies. As an engineer, he made repeated use of modeling to test the design of many of his inventions and projects. His understanding of the system of systems engineering was futuristic for his period in history. Still, he determined that by understanding how each separate machine part functioned, he could modify it and combine it with other parts in different ways to improve existing machines or create new machines. da Vinci provided one of the first systematic explanations of how machines work and how the elements of machines can be combined. Through the following centuries the military continued to use modeling as a means of training with live exercises and with games that would resemble table-top exercises.

The technical origins of M&S go back to 1929 with the Link Flight Simulator. As a training tool, this simulator proved to be greatly cost cutting and it was eventually adopted by all branches of the military. Throughout the twentieth century, the Department of Defense laid claim to M&S by engaging simulation training in large-scale exercises. By 1983, the Defense Advanced Research Projects Agency (DARPA) had initiated simulator networking (SIMNET) with an emphasis on tactical team performance on the battlefield. Advancements in computer software and hardware as well as artificial intelligence and software agents hastened the pace of the maturation of M&S as a discipline and opened the way for M&S as a multidisciplinary application or tool for research and analysis. By the turn of the twentieth century, advanced academic programs enabled engineering students to graduate with a Doctor of Philosophy (PhD) in M&S.²

The advancement in technical capacity as well as research and development (R&D) allows M&S to have at its disposal enhanced capabilities for modeling, simulating, and analyzing complex phenomena. The technical features, coupled with a clearer understanding and application of the numerous modeling paradigms, allow the modeler (developer) to represent both complicated systems and complex systems and this is important because there are significant differences in these systems. To understand how they differ, a review of what comprises a system is needed.

A system is a construct or collection of different elements that together produce results not obtainable by the elements alone. The elements to a system vary ranging from people to hardware to facilities to political structures to documents—any and all of the things required to produce system-level qualities, properties, characteristics, functions, behavior, and performance. Recall it was da Vinci who recognized that the value of the system as a whole is the relationship among the parts. A modeler must understand both the parts and the whole of a system.

There are two types of systems: discrete in which the variables change instantaneously at separate points in time and continuous where the state variables change continuously with respect to time (these systems and the simulations used to represent them will be discussed in greater detail under Section 1.3.1). So how do complicated and complex systems differ? They diverge on the basis of the level of understanding of the system; for example, a human system may have few parts but it is complex because it is difficult to ascertain absolutes in the data as human systems data is organic and dynamic. Thus, one cannot predict the behavior of a human system with any certainty. On the other hand, a finite element model or physics-based model may be complicated due to its numerous parts, but it is not complex in that it is predictable and the data to model such a system is not soft or fuzzy (unpredictable).

For the purposes of analysis there are three principle approaches to the study of a system: (i) the actual system versus a model of the system, (ii) a physical versus mathematical representation of the system, and (iii) analytical solution versus simulation solution (which exercises the simulation for inputs in question to see how they affect the output measures of performance) (4). Because M&S