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Modeling and Simulation Fundamentals: Theoretical Underpinnings
and Practical Domains
Description: An insightful presentation of the key concepts, paradigms, and applications of modeling and
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.
1 Introduction to Modeling and Simulation (Catherine M. Banks).
M&S Characteristics and Descriptors.
2 Statistical Concepts for Discrete Event Simulation (Roland R. Mielke).
Input Data Modeling.
Output Data Analysis.
3 Discrete-Event Simulation (Rafael Diaz and Joshua G. Behr).
Queuing System Model Components.
Hand Simulation—Spreadsheet Implementation.
4 Modeling Continuous Systems (Wesley N. Colley).
Modeling and Simulation (M&S) Strategy.
Simulating Continuous Systems.
5 Monte Carlo Simulation (John A. Sokolowski).
The Monte Carlo Method.
6 Systems Modeling: Analysis and Operations Research (Frederic D. McKenziei).
System Model Types.
Modeling Methodologies and Tools.
Analysis of Modeling and Simulation (M&S).
7 Visualization (Yuzhong Shen).
Computer Graphics Fundamentals.
Visualization Software and Tools.
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
The CountrySim Application and Sociocultural Game Results.
Conclusions and the Way Forward.
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.
10 Verifi cation, Validation, and Accreditation (Mikel D. Petty).
Background Defi nitions.
VV&A Defi nitions.
V&V as Comparisons.
VV&A Case Studies.
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.
12 Interoperability and Composability (Andreas Tolk).
Defining Interoperability and Composability.
Current Interoperability Standard Solutions.
Engineering Methods Supporting Interoperation and Composition.
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