Cover -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- Chapter 1: Simulation: History, Concepts, and Examples -- 1.1. Issues: simulation, a tool for complexity -- 1.1.1. What is a complex system? -- 1.1.2. Systems of systems -- 1.1.3. Why simulate? -- 1.1.4. Can we do without simulation? -- 1.2. History of simulation -- 1.2.1. Antiquity: strategy games -- 1.2.2. The modern era: theoretical bases -- 1.2.3. Contemporary era: the IT revolution -- 1.3. Real-world examples of simulation -- 1.3.1. Airbus -- 1.3.2. French defense procurement directorate -- 1.4. Basic principles -- 1.4.1. Definitions -- 1.4.2. Typology -- 1.5. Conclusion -- 1.6. Bibliography -- Chapter 2. Principles of Modeling -- 2.1. Introduction to modeling -- 2.2. Typology of models -- 2.2.1. Static/dynamic -- 2.2.2. Deterministic/stochastic -- 2.2.3. Qualities of a model -- 2.3. The modeling process -- 2.3.1. Global process -- 2.3.2. Formulation of the problem -- 2.3.3. Objectives and organization -- 2.3.4. Analysis of the system -- 2.3.5. Modeling -- 2.3.6. Data collection -- 2.3.7. Coding/implementation -- 2.3.8. Verification -- 2.3.9. Validation -- 2.3.10. Execution -- 2.3.11. Use of results -- 2.3.12. Final report -- 2.3.13. Commissioning/capitalization -- 2.4. Simulation project management -- 2.5. Conclusion -- 2.6. Bibliography -- Chapter 3. Credibility in Modeling and Simulation -- 3.1. Technico-operational studies and simulations -- 3.2. Examples of technico-operational studies based on simulation tools -- 3.2.1. Suppression of aerial defenses -- 3.2.2. Heavy helicopters -- 3.3. VV&A for technico-operational simulations -- 3.3.1. Official definitions -- 3.3.2. Credibility -- 3.3.3. Key players in the domain -- 3.4. VV&A issues -- 3.4.1. Elements concerned -- 3.4.2. Verification and validation techniques -- 3.4.3. VV&A approaches.

3.4.4. Responsibilities in a VV&A process -- 3.4.5. Levels of validation -- 3.4.6. Accreditation -- 3.5. Conclusions -- 3.5.1. Validation techniques -- 3.5.2. Validation approaches -- 3.5.3. Perspectives -- 3.6. Bibliography -- Chapter 4. Modeling Systems and Their Environment -- 4.1. Introduction -- 4.2. Modeling time -- 4.2.1. Real-time simulation -- 4.2.2. Step-by-step simulation -- 4.2.3. Discrete event simulation -- 4.2.4. Which approach? -- 4.2.5. Distributed simulation -- 4.3. Modeling physical laws -- 4.3.1. Understanding the system -- 4.3.2. Developing a system of equations -- 4.3.3. Discrete sampling of space -- 4.3.4. Solving the problem -- 4.4. Modeling random phenomena -- 4.4.1. Stochastic processes -- 4.4.2. Use of probability -- 4.4.3. Use of statistics -- 4.4.4. Random generators -- 4.4.5. Execution and analysis of results of stochastic simulations -- 4.5. Modeling the natural environment -- 4.5.1. Natural environment -- 4.5.2. Environment databases -- 4.5.3. Production of an SEDB -- 4.5.4. Quality of an SEDB -- 4.5.5. Coordinate systems -- 4.5.6. Multiplicity of formats -- 4.6. Modeling human behavior -- 4.6.1. Issues and limitations -- 4.6.2. What is human behavior? -- 4.6.3. The decision process -- 4.6.4. Perception of the environment -- 4.6.5. Human factors -- 4.6.6. Modeling techniques -- 4.6.7. Perspectives -- 4.7. Bibliography -- Chapter 5. Modeling and Simulation of Complex Systems: Pitfalls and Limitations of Interpretation -- 5.1. Introduction -- 5.2. Complex systems, models, simulations, and their link with reality -- 5.2.1. Systems -- 5.2.2. Complexity -- 5.2.3. The difficulty of concepts: models, modeling, and simulation -- 5.3. Main characteristics of complex systems simulation -- 5.3.1. Nonlinearity, the key to complexity -- 5.3.2. Limits of computing: countability and computability.

5.3.3. Discrete or continuous models -- 5.4. Review of families of models -- 5.4.1. Equational approaches -- 5.4.2. Computational approaches -- 5.4.3. Qualitative phenomenological approaches -- 5.4.4. Qualitative structuralist approach: application of category theory -- 5.5. An example: effect-based and counter-insurgency military operations -- 5.6. Conclusion -- 5.7. Bibliography -- Chapter 6. Simulation Engines and Simulation Frameworks -- 6.1. Introduction -- 6.2. Simulation engines -- 6.2.1. Evolution of state variables -- 6.2.2. Management of events and causality -- 6.2.3. Simulation modes -- 6.2.4. Example -- 6.3. Simulation frameworks -- 6.3.1. Some basic points on software engineering -- 6.3.2. Frameworks -- 6.3.3. Obstacles to framework use -- 6.3.4. Detailed example: ESCADRE -- 6.4. Capitalization of models -- 6.5. Conclusion and perspectives -- 6.6. Bibliography -- Chapter 7. Distributed Simulation -- 7.1. Introduction -- 7.1.1. The principle -- 7.1.2. History of distributed simulations -- 7.1.3. Some definitions -- 7.1.4. Interoperability in simulation -- 7.1.5. Standardization -- 7.1.6. Advantages and limitations of distributed simulation -- 7.1.7. Other considerations -- 7.2. Basic mechanisms of distributed simulation -- 7.2.1. Some key principles -- 7.2.2. Updating attributes -- 7.2.3. Interactions -- 7.2.4. Time management -- 7.2.5. Dead reckoning -- 7.2.6. Multi-level modeling -- 7.2.7. Section conclusion -- 7.3. Main interoperability standards -- 7.3.1. History -- 7.3.2. HLA -- 7.3.3. DIS -- 7.3.4. TENA -- 7.3.5. The future of distributed simulation: the LVC AR study -- 7.3.6. Other standards used in distributed simulation -- 7.4. Methodological aspects: engineering processes for distributed simulation -- 7.4.1. FEDEP -- 7.4.2. SEDEP -- 7.4.3. DSEEP -- 7.5. Conclusion: the state of the art: toward "substantive" interoperability.

7.6. Bibliography -- Chapter 8. The Battle Lab Concept -- 8.1. Introduction -- 8.2. France: Laboratoire Technico-Opérationnel (LTO) -- 8.2.1. Historical overview -- 8.2.2. Aims of the LTO -- 8.2.3. Principles of the LTO -- 8.2.4. Services of the LTO -- 8.2.5. Experimental process -- 8.2.6. Presentation of an experiment: PHOENIX 2008 -- 8.2.7. Evaluation and future perspectives of the LTO -- 8.3. United Kingdom: the Niteworks project -- 8.4. Conclusion and perspectives -- 8.5. Bibliography -- Chapter 9. Conclusion: What Return on Investment Can We Expect from Simulation? -- 9.1. Returns on simulation for acquisition -- 9.2. Economic analysis of gains from intelligent use of simulations -- 9.2.1. Additional costs of the SBA -- 9.2.2. Additional costs from unexpected events or bad planning -- 9.3. Multi-project acquisition -- 9.4. An (almost) definitive conclusion: conditions for success -- 9.5. Bibliography -- Author Biographies -- List of Authors -- Index.