Validation is no doubt one of the most important steps in the development of an effective and reliable simulation model of a real system. It aims at deciding whether the model forms a representation of the system accurate enough for credible analysis and decision making. The methods that are currently available for validation are binary in nature, in the sense that they can only be used to either reject or accept the validity of a model. Since it is a commonly accepted point of view that all models are invalid in the strict sense, we develop in this paper a new method for validation that allows to express degrees of validity on a continuous scale. The method makes use of a fuzzy inference algorithm and of a fairly new concept in the theory of fuzzy sets, known as resemblance relations. We demonstrate how our method can easily be used to discriminate more from less valid simulation models for a real-life airline network.
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