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Many simulation experiments require considerable computer time, so interpolation is needed for sensitivity analysis and optimization. The interpolating functions are 'metamodels' (or 'response surfaces') of the underlying simulation models. For sensitivity analysis and optimization, simulationists use different interpolation techniques (e.g. low-order polynomial regression or neural nets). This paper, however, focuses on Kriging interpolation. In the 1950's, D.G. Krige developed this technique for the mining industry. Currently, Kriging interpolation is frequently applied in computer aided engineering. In discrete event simulation, however, Kriging has just started. This paper discusses Kriging for sensitivity analysis in simulation, including methods to select an experimental design for Kriging interpolation.