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Baixar PDFNeimeier, Henry, "Analytic Uncertainty Modeling", 1994
- 1994
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The analytic uncertainty modeling techniques is useful whenever sensitivity analysis is important. It provides the entire resulting probability distribution instead of a single uncertain point estimate of the mean. Both analytic development costs, and computer execution cost are far less than in discrete event simulation. The price paid is some lack in modeling flexibility. Discrete simulation requires multiple long simulation runs to obtain a statistically significant point estimate. The different result values from multiple runs with identical parameter values but different random number seeds, are average to obtain the point estimate of the mean results value. Conversely, the analytic solution gives the entire resulting probability distribution with minimal calculation. The analytic solution also considerably simplifies sensitivity analysis. A single analytic run is done for each input parameter setting. Discrete event simulation requires multiple runs for each input parameter, to obtain a statistically significant mean result. In functional economic analysis we are interested in the relative future cost of alternative systems. There are uncertainties in process performance, resource requirements, cost estimate, investments required, workload, interest and inflation rates. There is also uncertainty in the future projection of these elements. Analytic uncertainty modeling provides a simple way of calculating output measure uncertainty from model input parameter uncertainties.
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