The paper analyzes the main causes of the (in)consistency of the selected method of multiplicative decision-making: data normalization, weight coefficients and the application of the Likert scale for the purpose of measuring quantitative attributes. Normalized data in the methods of multitributive decision making represents the substitute for a subjective attribute ratings by decision makers. Since they are calculated on the basis of mathematical transformations of empirical data, one gains the impression that the choices basen on normalized values are „objective”. Therefore, the sensitivity analysis of the results has dealt exclusively with effects of weight coefficients on the final choices so far, while the potential impact of normalization is complitely ignored; meanwhile, the deformations caused by the normalization of data have been attributed to the effects of weight coefficients and their inevitable subjectivism. We intent to point out at the deformations of empirical values that are the result of normalization and which call into question the application of normalized values as a deci-sion base. It can be proven that the normalized values are an unrealiableinformation base for decision-making. In addition, the (in)consistency ofselection methods of multi-attributive decision-making is also influencedby changes in the method of measuring and formulating attributes.
In real situations, the attribute value (mostly variable)can be best represented by introducing the finite numberof attribute values level, to which the correspondingprobabilities should also be attached. Stochastic Multi-Attribute Utility Model has the ability to analyze suchstochastic multi-attribute problems. The choice of one,from the set of available options, is made by choosingthe best option based on the maximum expected utilitystructure. In this paper, we will mention some argumentsfor the development of the Stochastic Multi-AttributeUtility Model, its advantages (they are closer to reality),disadvantages (analytically difficult technique, subjectiveassessments of the values of variable attributes), aswell as the process of solving the problem.
Real situations are based on multiple attributes or crite- ria. If the assessment problem has multiple dimensions of value, then intuitive judgments can be very difficult. In many situations, the ultimate assessment seems very difficult, especially when the decision maker chooses one of a set of possible options in an unfamiliar envi- ronment. This paper analyzes indifference value meas- urement methods. Indifference value measurement methods rely on studies of indifference (indifference assessments) or comparison of strength of preferences. The values that the decision maker gives to the attrib- utes reflect his preferences, ie. utilities. Utility is only a way to describe preferences. The process of determining (functions) values is only one form of sequential deter- mination of utility.
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