Research and Advances
Artificial Intelligence and Machine Learning

Methods for analyzing data from computer simulation experiments

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This paper addresses itself to the problem of analyzing data generated by computer simulations of economic systems. We first turn to a hypothetical firm, whose operation is represented by a single-channel, multistation queueing model. The firm seeks to maximize total expected profit for the coming period by selecting one of five operating plans, where each plan incorporates a certain marketing strategy, an allocation of productive inputs, and a total cost. The results of the simulated activity under each plan are subjected to an F-test, two multiple comparison methods, and a multiple ranking method. We illustrate, compare, and evaluate these techniques. The paper adopts the position that the particular technique of analysis (possibly not any one of the above) chosen by the experimenter should be an expression of his experimental objective: The F-test tests the homogeneity of the plans; multiple comparison methods quantify their differences; and multiple ranking methods directly identify the one best plan or best plans.

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