Abstract

This chapter discusses the issue of choosing the best computer model for simulating a real-world phenomenon through the process of validating the model’s output against the historical, real-worldData data. Four families of techniques are discussed that are used in the context of validation. One is based on the comparisonComparison of statistical summaries of the historical dataData and the model output. The second is used where the models and dataData are stochastic, and distributions of variables must be compared, and a metricMetric is used to measure their closeness. After exploring the desirable properties of such a measure, the paper compares the third and fourth methods of measuring closeness ofPattern patterns, using an example from strategic market competition. The techniques can, however, be used for validating computer models in any domain.