The problem of validating computer simulation models of industrial systems has received only limited attention in the management science literature. The purpose of this paper is to consider the problem of validating computer models in the light of contemporary thought in the fields of philosophy of science, economic theory, and statistics. In order to achieve this goal we have attempted to gather together and present some of the ideas of scientific philosophers, economists, statisticians, and practitioners in the field of simulation which are relevant to the problem of verifying simulation models. We have paid particular attention to the writings of economists who have been concerned with testing the validity of economic models. Among the questions which we shall consider are included: What does it mean to verify a computer model of an industrial system? Are there any differences between the verification of computer models and the verification of other types of models? If so, what are some of these differences? Also considered are a number of measures and techniques for testing the "goodness of fit" of time series generated by computer models to observed historical series.

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Why are over 2,000 corporations either using, developing, or planning to develop some form of corporate simulation model? What types of companies are using corporate planning models? How are they being used? Which resources are required? These are among the questions which were raised in a recent survey of 346 companies whose results are summarized in this paper. The paper also examines the costs and benefits to be derived from using corporate simulation models. Finally, drawing on the survey results, the authors speculate on future developments in the field of corporate modeling.

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Experimental designs have found widespread application in the biological and physical sciences. The purpose in using these designs is not only that they are economical in terms of the number of experimental trials required, but more importantly, they provide a structure for the investigator's learning process. Experimental designs are available for (1) investigating the relationship of the response to the factors in order to determine the underlying mechanisms governing the process under study and (2) finding the combination of factor levels at which the response variable is optimized. Since a computer simulation experiment is indeed an experiment, it follows that one should give careful attention to experimental design questions prior to conducting a simulation experiment. Within the context of two simulation experiments with an inventory model, this paper discusses factorial, fractional factorial, rotatable, and response surface designs. We conclude by outlining a number of possible pitfalls and contingencies.

This paper is concerned with the use of spectral analysis to analyze data generated by computer simulation experiments with models of economic systems. An example model serves to illustrate two different applications of spectral analysis. First, spectral analysis is used to construct confidence bands and to test hypotheses for the purpose of comparing the results of the use of two or more alternative economic policies. Second, spectral analysis is employed as a technique for validating an econometric model.

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