Technical Decision-Making Aids in .NET

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51.2 Technical Decision-Making Aids
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analysis of test results along with further validation of the models using the test data. (Source: DoD 5000.59-M Modeling and Simulation (M&S) Glossary, Part II, item 342, p. 137) Monte Carlo Algorithm A statistical procedure that determines the occurrence of probabilistic events or values of probabilistic variables for deterministic models; e.g., making a random draw. (DSMC 1993 94 Military Research Fellows Report, (reference (k)). (Source: DoD 5000.59-M Modeling and Simulation (M&S) Glossary, Part II, item 345, p. 138) Monte Carlo Method In modeling and simulation, any method that employs Monte Carlo simulation to determine estimates for unknown values in a deterministic problem. (DIS Glossary of M&S Terms, and IEEE STD 610.3, (references (b) and (c)). (Source: DoD 5000.59-M Modeling and Simulation (M&S) Glossary, Part II, item 346, p. 138) Simulation Time A simulation s internal representation of time. Simulation time may accumulate faster, slower, or at the same pace as sidereal time. (DIS Glossary of M&S Terms, and IEEE STD 610.3, (references (b) and (c)). (Source: DoD 5000.59-M Modeling and Simulation (M&S) Glossary, Part II, item 473, p. 159) Stimulate To provide input to a system in order to observe or evaluate the system s response. (Source: DSMC Simulation Based Acquisition: A New Approach Dec. 1998) Stimulation The use of simulations to provide an external stimulus to a system or subsystem. (Source: DSMC Simulation Based Acquisition: A New Approach Dec. 1998) Stochastic Process Any process dealing with events that develop in time or cannot be described precisely, except in terms of probability theory. (DSMC 1993 94 Military Research Fellows Report, (reference (k)). (Source: DoD 5000.59-M Modeling and Simulation (M&S) Glossary, Part II, item 493, p. 161) Validated Model An analytical model whose outputs and performance characteristics identically or closely match the products and performance of the physical system or device. Validation (Model) The process of determining the manner and degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model, and of establishing the level of con dence that should be placed on this assessment. (DSMC SE Fundamentals, Section 13.4 Veri cation, Validation, and Accreditation; p. 120.) Veri cation (Model) The process of determining that a model implementation accurately represents the developer s conceptual description and speci cations that the model was designed to. (DSMC SE Fundamentals, Section 13.4 Veri cation, Validation, and Accreditation; p. 120.)
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51.2 TECHNICAL DECISION-MAKING AIDS
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SE decision making related to system performance allocations, performance budgets and safety margins, and design requires decision support to ensure that informed, fact-based recommendations are made. Ideally, we would prefer to have an exact representation of the system you are analyzing. In reality, exact representations do not exist until the system or product is designed, developed, veri ed, and validated. There are, however, some decision aids SE can employ to provide degrees of representations of a system or product to facilitate technical decision making. These include models, prototypes, and mock-ups. The purpose of the decision aids is to create a representation of a system on a small, low-cost scale that can provide empirical form, t, or function data to support design decision making on a much larger scale.
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System Modeling and Simulation
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51.3 MODELS
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Models generally are of two varieties: deterministic and stochastic.
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Deterministic Models
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Deterministic models are structured on known relationships that produce predictable, repeatable results. Consider the following example: EXAMPLE 51.1
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Each work period an employee receives a paycheck based on a formula that computes their gross salary hours worked times hourly rate less any distributions for insurance, taxes, charitable contributions, and so forth.
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Stochastic Models
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Whereas deterministic models are based on precise relationships, stochastic models are structured using probability theory to process a set of random event occurrences. In general, stochastic models are constructed using data from statistically valid samples of a population that enable us to infer or estimate results about the population as a whole. Consider the following theoretical example: EXAMPLE 51.2
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A manufacturer produces 0.95 inch spacer parts in which three of the parts are assembled onto an axle. The axle is then installed into a constrained space of a larger ASSEMBLY. If we produced several thousand parts, we might discover the individual spacers randomly vary in size from 0.90 to 1.0 inch; no two parts are exactly the same. So, what must the dimension of the constrained space be to accommodate dimensional variations of each spacer We construct a stochastic model that randomly selects dimensions for each of three spacers within their allowable ranges. Then, it computes the integrated set of dimensions to estimate the mean of a typical stacked con guration. Based on the results, a dimension of the constrained space is selected that factors in any additional considerations such degrees of looseness, if applicable.
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The example above illustrates theoretically how a stochastic model could be employed. SEs often use a worst-case analysis in lieu of developing a model. For some applications this may be acceptable. However, suppose a worst-case analysis results in too much slack between the spools What the SEs need to know is: For randomly selected parts assembled into the con guration, based on frequency distributions of part dimensions and their standard deviations, WHAT is the estimated mean of any con guration In summary, stochastic models enable us to estimate or draw inferences about system performance for highly complex situations. These situations involve random, uncontrollable events or inputs that have a frequency of occurrence under prescribed conditions. Based on the frequency distributions of sampled data, we can apply statistical methods that infer a most probable outcome for a speci c set of conditions. Examples include environmental conditions and events, human reactions to publicity, and pharmaceutical drug medications.
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