There have been a number of multiattribute decision aids developed to aid selection problems. Multiattribute value theory and the analytic hierarchy process ~IE two commonly used techniques. Different systems can result in radically different conclusions if they inaccurately and inconsistently reflect the preference structure of decision makers, or if they are based on inappropriate theoretical models. This study examines the impact of the underlying theoretical model, the method in which preference information is elicited, and the structure of alternatives as influences on the results from using various decision aids. It was found that two systems based on the multiattribute value theory model were just as diverse in their conclusions as were results between AHP and the multiattribute value theory models. Therefore, accuracy of infomation reflecting decision maker preference is an important consideration. Feedback capable of assuring the decision maker that information provided is consistent is a necessary feature required of decision aids applied to selection problems. The study also found that the way in which information is elicited influenced the result more than did the underlying model. Exact numerical data for complex concepts such as attribute importance and alternative performance on attributes is not necessary, and elicitation procedures that axe more natural for the user are likely to be more accurate.

Very little work has been done in developing a systematic and exhaustive set of criteria which can be used for assessing model validity from a managerial standpoint. Most of the other published work in this area focuses on an individual criterion, such as sensitivity analysis. This manuscript offers a set of criteria making that assessment. The following three basic kinds of validity are proposed: (1) technical validity; (2) operational validity; and (3) dynamic validity. Each has a number of subcriteria which are identified and discussed. Some of these criteria are quantifiable, and some are not. Each of the criterion identified is discussed in relatively broad terms, with brief examples.One of the purposes of this paper is to draw attention to an area worthy of further investigation. Perhaps further scrutiny will generate a truly systematic and exhaustive set of criteria. It is also hoped that further attention will lead to clarification of the criteria identified. PURPOSEThis paper attempts to identify a systematic and exhaustive set of criteria which can be used for assessing model validity. Most quantitative texts make passing reference to model validity. A few paragraphs or sentences are spent pointing out that the model must provide an accurate reflection of the environment modeled. Some texts point out the critical role of managerial judgment, indicating that it is the manager's role to accept or reject the input of the analyst. This author knows of no text which establishes a systematic and exhaustive set of criteria to judge the analyst's output. Certain selected techniques, such as sensitivity analysis, provide useful information but often are more technical than managerial in focus. Statistical analysis of the difference between actual and projected outcomes is another technique which has proven useful (especially if the parameter conditions remain stable).This paper offers a tentative set of criteria which the author believes are systematic and exhaustive. Further, he believes that the concepts underlying each are sufficjently clear so that the manager and the analyst can use these as a basis for judging model validity from a managerial standpoint. It is the purpose of this paper to draw attention to an area worthy of further investigation. The presentation is nontechnical, in the hope that it will prove a useful starting point for both analyst and manager.This discussion is expected to be helpful to both the manager and the analyst. The manager may ask the analyst to provide information on each of these criteria;

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