The primary goal of research in multiple criteria decision analysis is to develop tools to help people make more reasonable decisions. In many cases, the development of such tools requires the combination of knowledge derived from such areas as applied mathematics, cognitive psychology, and organizational behavior. Verbal Decision Analysis (VDA) is an example of such a combination. It is based on valid mathematical principles, takes into account peculiarities of human information processing system, and fits the decision process into existing organizational environments. The basic underpinnings of Verbal Decision Analysis are demonstrated by early VDA methods, such as ZAPROS and ORCLASS. New trends in their later modifications are discussed. Published applications of VDA methods are presented to support the findings. 1. Introduction A large number of decision making situations require taking into consideration multiple, often conflicting, influencing factors or criteria. A number of different multiple criteria decision aids (MCDA) have been developed to support this class of decision problems. In the majority of multiple criteria tasks, the “objectively” best alternative may often not be defined. As a result, in spite of crucial differences in approaches, many methods dealing with multiple criteria environments are based on subjective information about relative importance of objectives and criteria to the decision maker. The subjective nature of information in the majority of multiple criteria models makes it a challenge to obtain consistent and accurate results in such tasks. As a result, elicitation of the decision makers’ preferences should take into account peculiarities of human behavior in the decision processes. This is the central goal of Verbal Decision Analysis or VDA [1, 2]. The Verbal Decision Analysis is a framework for designing methods of MCDA by using preferential information from the decision makers in the ordinal form, a type of judgment known to be much more stable and consistent. VDA is based on the same principles as multiattribute utility theory (MAUT) [3] but is oriented on using the verbal form of preference elicitation and on evaluation of alternative decisions without resort to numbers. Traditional methods of VDA were oriented on problems with a rather large number of alternatives but a relatively small number of criteria. They were designed to elicit a sound preference relationship that could be applied to future sets of real alternatives, while many other methods, for example, outranking methods [4] or AHP [5], tended to
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