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A physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process.
-Representation of something else, often a "real-world" system -Some aspects of the modeled system are represented in the model, others not |
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Executing a model over time. Also, a technique for testing, analysis, or training in which real world systems are used, or where a model reproduces real world and conceptual systems. |
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A single execution of a simulation. |
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A run, or series of related runs, of a simulation being used for training. |
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A run, or series of related runs, of a simulation being used for analysis or experimentation. |
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The use of models and simulations, either statically or over time, to develop data as a basis for making managerial or technical decisions. This includes, but is not limited to, emulators, prototypes, simulators, and stimulators. |
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The subject of a model or simulation.
-Real world system -Thing to be simulated |
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-Intended uses -Needed validity, resolution, scale |
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-Simuland components, structure -Aspects of simuland to model -Implementation specifications -Use cases -Assumptions -Initial model parameter values |
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-Computer software -Implemented conceptual model |
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-Output of model -Produced during simulation |
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Person who participates in the simulation and is expected to benefit from the experience. |
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Person who operates all or part of the model during the simulation, but is not expected to benefit from the experience. |
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Person who uses simulation output to gain insight into the simulated systems or scenario. |
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The degree of detail with which the real-world is simulated. More detail is higher resolution.
Low - Simulate attack squadron as a whole.
Medium - Simulate individual aircraft
High - Simulate sensor and weapons systems |
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The body of knowledge available to a modeler regarding a simuland.
Quantitative and formal -physics equations for aircraft flight dynamics
Qualitative and informal -pilot's expectation of buffet before stall |
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Accuracy of model's representation or simulation's results.
Validity is relative to -Reality -Representation -Requirements |
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Size of the overall scenario or event the simulation represents. |
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A category of models or simulations with some important common characteristics.
Defining characteristics -Intent or goal of simulation -Presence of randomness -Handling of time -Degree of "virtuality" |
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Modeling Method (Model Type) |
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Basis or method underlying a model; the way the model represents its subject.
Characteristics of each model type -Representational basis -feature and characteristics -advantages and disadvantages -typical uses |
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M&S used to produce learning in a user or participant. |
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M&S used to explore design or solution spaces, or to gain insight into a incompletely understood situation.
An iterative process of collecting, developing and exploring concepts to identify and recommend the best value-added solutions for changes in the doctrine, organization, training, material, leadership and people required to achieve significant advances in future joint operational capabilities.
-Less controlled than analysis -Simulation used to "explore" possibilities |
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M&S used to predict, design, test, or evaluate a real or notional system or idea.
-Simulation used to answer questions -Repeatability often desirable -Careful experimental design |
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M&S used to develop, analyze, or test an engineering design.
-Important simuland aspects modeled -Models physics-based, no "behavior" -No virtual environment or simulators -User not expected to benefit from experience -Primary goal: useful result |
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Acquisition (Simulation based Acquisition) |
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M&S used to specify, design, develop, and acquire new systems.
-Simulation used to acquire systems -Goal: save lives, money, time |
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M&S used to entertain or amuse a user or participant. |
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Simulation where a given set of inputs will produce a determined, unique set of outputs |
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