Body of Knowledge for Modeling and Simulation: A Handbook by the Society for Modeling and Simulation International

Preface
Contents
Contributors
1 Preliminary
	Abstract
	1.1 Scope
	1.2 Terminology
	1.3 Rationale for Theoretical Basis of M&S
	1.4 Modeling and Simulation Framework (MSF)
		1.4.1 System Concepts
			1.4.1.1 System Specification Hierarchy Levels of System Specification
		1.4.2 The Entities of the Modeling and Simulation Framework
			1.4.2.1 Source System
			1.4.2.2 Experimental Frame
			1.4.2.3 Objectives and Experimental Frames
			1.4.2.4 Simulator
		1.4.3 Primary Relations Among Entities
			1.4.3.1 Modeling Relation: Validity
			1.4.3.2 Simulation Relation: Simulator Correctness
		1.4.4 Other Important Relationships
			1.4.4.1 Modeling as Valid Simplification
			1.4.4.2 Experimental Frame—Model Relationships
		1.4.5 Time
		1.4.6 Mapping Informal Terminology to MSF Formalization
	1.5 Basic System Entity Structure (SES) Concepts
	References
2 M&S Bok Core Areas and the Big Picture
	Abstract
	2.1 The Big Picture
	2.2 Data
	2.3 Models and Modeling Formalisms
	2.4 Model Engineering
		2.4.1 Model Life Cycle
		2.4.2 Definition of Model Engineering Model Life Cycle
		2.4.3 Key Technologies of Model Engineering
		2.4.4 General Technologies
			2.4.4.1 Modeling of Model Lifecycle Process
			2.4.4.2 Model Engineering Lifecycle Management
		2.4.5 Model Construction Technologies
			2.4.5.1 Acquisition and Management of Model Requirements
			2.4.5.2 Model Specification and Language
			2.4.5.3 Modeling of Process Management
		2.4.6 Model Management Technologies
			2.4.6.1 Model Library
			2.4.6.2 Model Evolution
			2.4.6.3 Model Reconfiguration
				Lower-Layer Reconfiguration
				Upper-Layer Reconfiguration
			2.4.6.4 Model as a Service
			2.4.6.5 Model Composition
				Offline Model Composition
				Online Model Composition
		2.4.7 Analysis and Evaluation Technologies
			2.4.7.1 The VV&A of a Model
			2.4.7.2 The Evaluation of the Whole Process of ME
			2.4.7.3 Model Maturity Definition and Evaluation
		2.4.8 Supporting Technologies
	2.5 Model Curation for Repository Integration: Discovery, Composition, and Reuse
	2.6 Model-Based Simulation
		2.6.1 Model-Based/driven Approaches
		2.6.2 Simulation-Based/Driven Approaches
		2.6.3 Models Simulations-Based/Driven Approaches
	2.7 Transient Elimination in Simulation
		2.7.1 Introduction
		2.7.2 Techniques to Eliminate Transient Results
		2.7.3 Stopping Criteria for Simulations
		2.7.4 Conclusion
	References
3 Simulation as Experimentation
	Abstract
	3.1 Types of Experimentations
	3.2 Reasons to Use Simulation Experiments
		3.2.1 Aren’t the Reasons Obvious?
		3.2.2 Relying on Data is Often Impossible
		3.2.3 Even if the Data Exists, It’s Not Enough: Understanding and Reasoning Demand Causal Models and, Sometimes, Systematic Experimentation
		3.2.4 Planning Toward Objectives and Avoiding or Managing Failures
		3.2.5 Cautions
			3.2.5.1 Model Validity
			3.2.5.2 Analysis Under Deep Uncertainty
	3.3 Types of Simulation Techniques for Experimentation
		3.3.1 Sections of BoK Guide Related to Experimentation
		3.3.2 Simulation Experimentation for all 3 Types of System Problems
		3.3.3 Relationship of Operations of Simulation and Real System
		3.3.4 Use of Simulation for Decision Support
		3.3.5 Statistical Experiment Design Techniques
	3.4 Simulation of Discrete Systems, DEVS
	3.5 Simulation of Continuous Systems
	3.6 Hybrid Modelling and Simulation
		3.6.1 From Complexity to Hybridization
		3.6.2 Definitions
		3.6.3 The Unified Conceptual Representation of Hybrid Modelling and Hybrid Simulation
		3.6.4 Classification of Hybrid Modelling and Simulation into Distinct Model Types
	3.7 Real-Time Discrete-Event Simulation
		3.7.1 Overview:
		3.7.2 Two Forms of RT-DES
	3.8 Simulation of Comprehensive Systems
		3.8.1 Connotation
		3.8.2 The Technical Framework
		3.8.3 Key Technologies
		3.8.4 Development Trend
		3.8.5 Application
	References
4 Simulation as Experience to Enhance Three Types of Skills
	Abstract
	4.1 Types of Simulation Techniques for Experience
		4.1.1 Simulation Experience for Training
		4.1.2 Simulation Experience for Entertainment
	4.2 Virtual Simulation
	4.3 Constructive Simulation
		4.3.1 Examples for Constructive Simulation
		4.3.2 Serious Games
		4.3.3 Additional Application Domains of Interest
	4.4 Live Simulation
		4.4.1 Examples for Live Simulation
		4.4.2 Live Simulation in the Context of LVC Architectures
		4.4.3 Enhancing Live Simulation with Augmented Reality
		4.4.4 Ethical Constraints and Conclusion
	References
5 Simulation Games
	Abstract
	5.1 Scope
	5.2 Terminology
	5.3 Applications
	References
6 Infrastructure
	Abstract
	6.1 Standards
		6.1.1 De Jure, De Facto, and Proprietary Standards
		6.1.2 Open Standards
		6.1.3 Standards Organizations
		6.1.4 M&S Standards Organizations
		6.1.5 Compliance Certification
	6.2 Code of Best Practice
	6.3 Lessons Learned
	6.4 Resource Repositories
	6.5 Distributed Interactive Simulation (DIS)
		6.5.1 Simnet
		6.5.2 Origins of the DIS Protocol
		6.5.3 DIS Today
	6.6 High Level Architecture (HLA)
	References
7 Reliability and Quality Assurance of M&S
	Abstract
	7.1 Errors—Types and Sources
		7.1.1 Definitions
		7.1.2 Types of Errors
		7.1.3 Terms Related with Errors
		7.1.4 Terms (Other Than Error) Related with Failure
		7.1.5 Other Sources of Failure
	7.2 Need for Reliability (Including Philosophical/Ethical Considerations About Reliability)
	7.3 Validation
		7.3.1 Introduction
			7.3.1.1 Validity: Perhaps a Misnomer, but One We Can Live with
			7.3.1.2 Over Interpreting the Cautions
			7.3.1.3 Definitions
		7.3.2 Distinctions
		7.3.3 Generalizing the Concept of Validation
			7.3.3.1 Rethinking the Concept of Validation
	7.4 Verification
		7.4.1 Introduction
		7.4.2 Intended Uses and Experimental Frames
		7.4.3 Integration of Simulation and Formal Verification
		7.4.4 Morphisms and Preservation of Properties
			7.4.4.1 Probabilistic Perspective: Bayesian Reasoning
		7.4.5 Summary
	7.5 Failure Avoidance
	References
8 Ethics
	Abstract
	8.1 Branches of Ethics
		8.1.1 Ethics in Technical Disciplines
		8.1.2 The Ethics of Computer Simulation
	8.2 Ethics for Simulationists and Analysts Using Modeling and Simulation
		8.2.1 Definitions
		8.2.2 Ethics in the Cycle of Modeling and Analysis
		8.2.3 Why Ethical Considerations Matter
		8.2.4 Approaches to Applying Ethics
		8.2.5 The Role of Professional Codes
		8.2.6 A New Obligation for Those Who Build M&S and Use It for Analysts
		8.2.7 Final Observation
	8.3 Code of Ethics for Simulationists
	References
9 Enterprise Modeling and Simulation
	Abstract
	9.1 Introduction
		9.1.1 Problem Statement About Enterprise Modeling and Simulation
		9.1.2 Methodological and Technical Approach
	9.2 Enterprise Modeling
		9.2.1 GRAI Model and GRAI Formalisms
		9.2.2 BPMN
		9.2.3 Other Formalisms for Information System Design
		9.2.4 Conclusions
	9.3 Driving Models to Simulation
		9.3.1 Interoperability
		9.3.2 Vertical Decomposition: Toward Alignment from Business to Operational
		9.3.3 Horizontal Alignment: Toward Simulation for Better Collaboration Between Service Network
	9.4 Implementing Framework and Method in MSTB Evolved
		9.4.1 Models and Model Transformation in MSTB (BSM Level)
		9.4.2 Using GRAI Grid and Extended Actigram* at Top BSM
		9.4.3 Domain Specific Languages at Bottom BSM
		9.4.4 Interface Process Model at TIM Level
		9.4.5 Simulation Model Orchestration at TIM Run Time
		9.4.6 Physical Infrastructure Interoperability with Simulation Model at TSM
		9.4.7 MDISE and MSTB Evolved for CPS
	9.5 Discussion and Conclusion
	References
10 Maturity and Accreditation
	Abstract
	10.1 Models, Programs, Processes, Individuals, and Organizations
	10.2 Educational Programs
		10.2.1 Academic Education
		10.2.2 Professional Education
		10.2.3 U.S. Army M&S Professional Support
	References
11 Supporting Computer Domains
	Abstract
	11.1 Digital Simulation
	11.2 Mobile Simulation
	11.3 Wearable Simulation
	11.4 Cloud-Based Modeling and Simulation
		11.4.1 Community Efforts and Emerging Standards
		11.4.2 Theoretical Frameworks
		11.4.3 Essential Infrastructure
		11.4.4 Illustrative Use Case Application
		11.4.5 Synopsys
	11.5 Quantum Simulation
		11.5.1 Simulation of Quantum Systems
		11.5.2 Simulation Performed on Quantum Computers
	11.6 High-Performance Simulation
		11.6.1 Connotation
		11.6.2 Technology System
		11.6.3 Key Technology
		11.6.4 Development Trend
		11.6.5 Application Case
	11.7 Parallel Evolutionary Algorithms
	11.8 Extreme-Scale Simulation
	References
12 Synergies of Soft Computing and M&S
	Abstract
	12.1 Fuzzy Logic and Fuzzy Simulation
	12.2 Neural Networks and Neural Network Simulation
	12.3 Synergies of Artificial Intelligence and M&S
		12.3.1 Contribution of M&S to Artificial Intelligence
		12.3.2 Contribution of Artificial Intelligence to M&S
	12.4 Example: Ant Colony Simulation
	12.5 Synergies of Agents and M&S
	12.6 Facets of Agent-Directed Simulation
		12.6.1 Agent Simulation or Agent-Based Simulation
		12.6.2 Agent-Monitored Simulation
		12.6.3 Agent-Supported Simulation
			12.6.3.1 Agent Support for Front-End Interfaces
			12.6.3.2 Agent Support for Back-End Interfaces
			12.6.3.3 Agent Support to Provide Cognitive Abilities to the Elements of M&S Systems
			12.6.3.4 Agent Support for Symbolic Processing of Elements of M&S Studies
	Appendix 1: Types of agents (adapted from Ören [53] and TBD-dic [55])
	Appendix 2: Agent-related concepts (adapted from Ören [53] and TBD-dic [55])
	References
13 Supporting Science Areas
	Abstract
	13.1 Systems Science
	13.2 Mathematics: Differential Equations
	13.3 Mathematics: Probability
	13.4 Mathematics: Frequently Used Distributions in Simulation and Their Characteristics and Applications
		13.4.1 Exponential Distribution
		13.4.2 Normal (Gaussian) Distribution
		13.4.3 Poisson Distribution
		13.4.4 Uniform Distribution
		13.4.5 Multinomial Distribution
		13.4.6 Log-Normal Distribution
		13.4.7 Weibull Distribution
		13.4.8 Pareto Distribution
		13.4.9 Geometric Distribution
		13.4.10 Gamma Distribution
		13.4.11 Inverse Gamma Distribution
		13.4.12 Erlang Distribution
		13.4.13 Beta Distribution
		13.4.14 Binomial Distribution
		13.4.15 Negative Binomial Distribution
		13.4.16 Chi-Square Distribution
		13.4.17 F-Distribution
		13.4.18 Student’s t-Distribution
		13.4.19 Concluding Remarks
	13.5 Queuing Theory
	13.6 Statistics
	References
14 Supporting Engineering Areas
	Abstract
	14.1 Systems Engineering
		14.1.1 M&S-Based Systems Engineering
		14.1.2 Simulation Systems Engineering
		14.1.3 Bridging the Gap Between M&S and Systems Life Cycle Processes
	14.2 VR/AR Engineering
		14.2.1 Connotation of VR/AR Engineering
		14.2.2 Architecture
		14.2.3 Key Technologies
			14.2.3.1 Human–Computer Interaction Technology
			14.2.3.2 Haptic Feedback Technology
			14.2.3.3 Environment and Object Modeling Technology
			14.2.3.4 System Integration Technology
			14.2.3.5 3D Registration Technology
			14.2.3.6 3D/4D Display and Stereo Synthesis Technology
			14.2.3.7 Virtuality and Reality Fusion-Based Display Technologies
		14.2.4 Development Trend
			14.2.4.1 Miniaturization and Mobility
			14.2.4.2 High Fidelity
			14.2.4.3 High-Virtuality-Reality Fusion
			14.2.4.4 Networking
			14.2.4.5 Intelligence
		14.2.5 Applications
	14.3 Visualization for Modeling and Interactive Simulation
		14.3.1 Visual Component Modeling
		14.3.2 Visual Interactive Simulation
		14.3.3 Visualization Designs
	References
15 Supporting Social Science and Management Areas
	Abstract
	15.1 Background: Contrasts of Paradigm and Semantics
		15.1.1 Theory
		15.1.2 Models
		15.1.3 Explanation
		15.1.4 Causal Variables
		15.1.5 Prediction
		15.1.6 Evaluating Models
	15.2 Supplementing Traditional Methods of Social Science (and Other Type-A Endeavors)
		15.2.1 Classic System Methods
		15.2.2 Newer Themes
			15.2.2.1 Qualitative Modeling
			15.2.2.2 Agent-Based Generative Modeling (and Inverse Generative Modeling)
	15.3 Aiding Strategic Decision-Making
		15.3.1 Dealing with Uncertainty and Disagreement
		15.3.2 Designing for Exploratory Analysis
		15.3.3 Designing Decision Aids with Education and Experience-Building in Mind
	References
16 Philosophy and Modeling and Simulation
	Abstract
	16.1 Philosophical Discussion of Simulation Epistemology
		16.1.1 Concepts and Definitions
			16.1.1.1 Ontology
			16.1.1.2 Epistemology
		16.1.2 Ontology and Simulation
		16.1.3 Epistemology of Simulation
	16.2 Scientific Research Methods and Simulation
		16.2.1 A Timeline of Scientific Research Method Developments
		16.2.2 The Increasing Role of Simulation with the Scientific Methods
		16.2.3 Modern Role of Models in the Scientific Method
		16.2.4 Simulation-Based Experiments Supporting Scientific Methods
		16.2.5 Conclusion on the Role of Modeling and Simulation
	16.3 What Type of Knowledge Can Be Acquired from Simulation?
	16.4 Criteria for Provisional Acceptance in Science: Objectivity, Completeness, and Reproducibility
	16.5 Hypothesis/Proposing Explanation in Simulation
		16.5.1 What Can Be Expected of the Explanatory Relation?
		16.5.2 Description, Prediction, Confirmation, and Explanation
		16.5.3 Can Computer Simulations Explain?
		16.5.4 Generating/Testing Hypotheses with Simulations
	16.6 Experiments Versus Simulation
	16.7 Experience Versus Simulation
	16.8 Simulation in the Spotlight of Cutting-Edge Technologies
	16.9 Modeling and Simulation (M&S) as a Discipline
	References
17 History of Simulation
	Abstract
	17.1 General History of Modeling and Simulation
		17.1.1 “Modeling and Simulation” in Pre-History
		17.1.2 Continuous System Simulation
		17.1.3 Event-Oriented Simulation
		17.1.4 Discrete Event Simulation Programing Languages
		17.1.5 Model-Based Simulation
	17.2 History of Simulation for Experimentation on Digital Computers
	17.3 Evolution of Computational Modeling and Simulation for Problem Solving
		17.3.1 Monte Carlo Modeling
		17.3.2 Emergence of Simulation Modeling Languages
		17.3.3 The Arrival of Simulation Programs
		17.3.4 Expansion of Simulation
		17.3.5 Simulation and Paradigm Shifts
		17.3.6 The Simulation Research that Advanced the Simulation Use
		17.3.7 Simulation as the Method of Choice
	17.4 Previous Studies on M&S BoK
	References
18 Core Research Areas
	Abstract
	18.1 Conceptual Modeling
	18.2 Model Reuse
	18.3 Embedded Simulation/Ubiquitous Simulation
		18.3.1 Connotation
		18.3.2 Technology System
			18.3.2.1 Modeling Theory and Methodology
			18.3.2.2 Simulation System Theory and Technology
			18.3.2.3 Simulation Application Engineering Theory and Technology
		18.3.3 Key Technologies
			18.3.3.1 Embedded Simulation/Ubiquitous Simulation Modeling Technology
			18.3.3.2 Embedded Simulation/Ubiquitous Simulation System Architecture
			18.3.3.3 Software Platform and Middleware of Embedded Simulation/Ubiquitous Simulation System
			18.3.3.4 Perception and Interaction Technologies Between Human and Embedded Simulation/Ubiquitous Simulation Service
			18.3.3.5 Simulation Service Migration Technology for Embedded Simulation/Ubiquitous Simulation Mode
			18.3.3.6 Coordinated Management and Integration Technologies of Simulation Space and Physical/Logical Space
			18.3.3.7 System Security Technology
			18.3.3.8 Embedded Simulation/Ubiquitous Simulation Application Technologies
		18.3.4 Development Tendency
			18.3.4.1 Ubiquitous Simulation Service
			18.3.4.2 Ternary Integration of “Human-Cyber-Physical”
			18.3.4.3 Ubiquitous Human–Computer Interaction
		18.3.5 Application Scenarios
			18.3.5.1 Intelligent Manufacturing
			18.3.5.2 Intelligent Transportation
			18.3.5.3 Intelligent Training
	18.4 Data-Driven M&S
		18.4.1 Introduction
		18.4.2 Data Assimilation Techniques
		18.4.3 An Example: Real-Time Scheduling of Cloud Manufacturing Services Based on Dynamic Data-Driven Simulation
			18.4.3.1 Model of DCMS Problem
			18.4.3.2 The Dynamic Scheduling Method
			18.4.3.3 System Framework
			18.4.3.4 Scheduling Rules
			18.4.3.5 DDDS Strategies
	18.5 Research Enabled by the M&S Framework: Application to Neuromorphic Architecture Design
	18.6 Model Behavior Generation for Multiple Simulators
	18.7 Simulation-Based Disciplines
	References
19 Trends, Desirable Features, and Challenges
	Abstract
	19.1 Trends
		19.1.1 New Digital/Data-Based Modeling and Simulation Technology
		19.1.2 New Virtual/Augmented Reality Modeling and Simulation Technology
		19.1.3 New High-Performance/Parallel Modeling and Simulation Technology
		19.1.4 New Networked/Cloud-Based Modeling and Simulation Technology
		19.1.5 New Intelligent Modeling and Simulation Technology
		19.1.6 New Ubiquitous Modeling and Simulation Technology
	19.2 Desirable Features
		19.2.1 Ideal Characteristics in Simulation Modeling Theory, Method, and Technology
		19.2.2 Ideal Characteristics in Simulation Systems and Supporting Technologies
		19.2.3 Ideal Characteristics in Simulation Application Engineering Technology
	19.3 Challenges
		19.3.1 Challenges to Simulation Modeling Theory and Methods
			19.3.1.1 Challenges to Virtual Reality Modeling Theory and Methods
			19.3.1.2 Challenges to Networked Simulation Modeling Theory and Methods
			19.3.1.3 Challenges to Intelligent Simulation Modeling Theory and Methods
			19.3.1.4 Challenges to High-Performance Simulation Modeling Theory and Methods
			19.3.1.5 Challenges to Data-Driven Simulation Modeling Theory and Methods
			19.3.1.6 Challenges to New AI-Based Simulation Modeling Theory and Methods
			19.3.1.7 Challenges to Simulation Modeling Theory and Method of System-of-Systems Confrontation
			19.3.1.8 Challenges to the Unified Simulation Modeling Theory Based on DEVS
		19.3.2 Challenges to Simulation Systems and Supporting Technology
			19.3.2.1 Challenges to Virtual Reality System and Supporting Technology
			19.3.2.2 Challenges to Networked Simulation System and Supporting Technology
			19.3.2.3 Challenges to Intelligent Simulation System and Technology
			19.3.2.4 Challenges to High-Performance Simulation System and Supporting Technologies
			19.3.2.5 Challenges to Data-Driven Simulation System and Technology
			19.3.2.6 Challenges to New AI-Based Simulation Systems and Supporting Technologies
			19.3.2.7 Challenges to Simulation Systems and Supporting Technologies of System-of-Systems Confrontation
			19.3.2.8 Challenges to DEVS-Based Simulation Interoperability Supporting Technology
		19.3.3 Challenges to Simulation Application Technology
			19.3.3.1 Challenges to Virtual Reality Application Engineering
			19.3.3.2 Challenges to Networked Simulation Application Engineering
			19.3.3.3 Challenges to Intelligent Simulation Application Engineering
			19.3.3.4 Challenges to High-Performance Simulation Application Engineering
			19.3.3.5 Challenges to Data-Driven Simulation Application Engineering
			19.3.3.6 Challenges to New AI-Based Simulation Application Engineering
			19.3.3.7 Challenges to Simulation Application Engineering of System-of-Systems Confrontation
			19.3.3.8 Challenges to DEVS-Based Simulation Application Engineering
	References
Appendix A: Terminology and Other Reference Documents
Appendix B: Bios of the Contributors
Index