Modeling and Simulation Fundamentals

An insightful presentation of the key concepts, paradigms, and applications of modeling and simulation Modeling and simulation has become an integral part of research and development across many fields of study, having evolved from a tool to a discipline in less than two decades. Modeling and Simulation Fundamentals offers a comprehensive and authoritative treatment of the topic and includes definitions, paradigms, and applications to equip readers with the skills needed to work successfully as developers and users of modeling and simulation. Featuring contributions written by leading experts in the field, the book′s fluid presentation builds from topic to topic and provides the foundation and theoretical underpinnings of modeling and simulation. First, an introduction to the topic is presented, including related terminology, examples of model development, and various domains of modeling and simulation. Subsequent chapters develop the necessary mathematical background needed to understand modeling and simulation topics, model types, and the importance of visualization. In addition, Monte Carlo simulation, continuous simulation, and discrete event simulation are thoroughly discussed, all of which are significant to a complete understanding of modeling and simulation. The book also features chapters that outline sophisticated methodologies, verification and validation, and the importance of interoperability. A related FTP site features color representations of the book′s numerous figures. Modeling and Simulation Fundamentals encompasses a comprehensive study of the discipline and is an excellent book for modeling and simulation courses at the upper–undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of computational statistics, engineering, and computer science who use statistical modeling techniques.

Preface. Contributors. 1 Introduction to Modeling and Simulation  ( Catherine M. Banks ). M&S. M&S Characteristics and Descriptors. M&S Categories. Conclusion. References. 2 Statistical Concepts for Discrete Event Simulation  ( Roland R. Mielke ). Probability. Simulation Basics. Input Data Modeling. Output Data Analysis. Conclusion. References. 3 Discrete–Event Simulation  ( Rafael Diaz and Joshua G. Behr ). Queuing System Model Components. Simulation Methodology. DES Example. Hand Simulation—Spreadsheet Implementation. Arena Simulation. Conclusion. References. 4 Modeling Continuous Systems  ( Wesley N. Colley ). System Class. Modeling and Simulation (M&S) Strategy. Modeling Approach. Model Examples. Simulating Continuous Systems. Simulation Implementation. Conclusion. References. 5 Monte Carlo Simulation  ( John A. Sokolowski ). The Monte Carlo Method. Sensitivity Analysis. Conclusion. References. 6 Systems Modeling: Analysis and Operations Research  ( Frederic D. McKenziei ). System Model Types. Modeling Methodologies and Tools. Analysis of Modeling and Simulation (M&S). OR Methods. Conclusion. References. Further Readings. 7 Visualization  ( Yuzhong Shen ). Computer Graphics Fundamentals. Visualization Software and Tools. Case Studies. Conclusion. References. 8 M&S Methodologies: A Systems Approach to the Social Sciences  ( Barry G. Silverman, Gnana K. Bharathy, Benjamin Nye, G. Jiyun Kim, Mark Roddy, and Mjumbe Poe ). Simulating State and Substate Actors with CountrySim: Synthesizing Theories Across the Social Sciences. The CountrySim Application and Sociocultural Game Results. Conclusions and the Way Forward. References. 9 Modeling Human Behavior  ( Yiannis Papelis and Poornima Madhavan ). Behavioral Modeling at the Physical Level. Behavioral Modeling at the Tactical and Strategic Level. Techniques for Human Behavior Modeling. Human Factors. Human–Computer Interaction. Conclusion. References. 10 Verifi cation, Validation, and Accreditation  ( Mikel D. Petty ). Motivation. Background Defi nitions. VV&A Defi nitions. V&V as Comparisons. Performing VV&A. V&V Methods. VV&A Case Studies. Conclusion. Acknowledgments. References. 11 An Introduction to Distributed Simulation  ( Gabriel A. Wainer and Khaldoon Al–Zoubi ). Trends and Challenges of Distributed Simulation. A Brief History of Distributed Simulation. Synchronization Algorithms for Parallel and Distributed Simulation. Distributed Simulation Middleware. Conclusion. References. 12 Interoperability and Composability  ( Andreas Tolk ). Defining Interoperability and Composability. Current Interoperability Standard Solutions. Engineering Methods Supporting Interoperation and Composition. Conclusion. References. Further Readings. Index.