Fuzzy Decision Theory Intelligent Ways for Solving Real-World Decision Problems and for Solving Information Costs

Abstract

Looking at modern theories in management science and business administration, one recognizes that many of these conceptions are based on decision theory in the sense of von Neumann and Morgenstern. However, empirical surveys reveal that the normative decision theory is hardly used in practice to solve real-life problems. This neglect of recognized classical decision concepts may be caused by the fact that the information necessary for modeling a real decision problem is not available, or the cost for getting this information seems too high. Subsequently, decision makers (DM’s) abstain from constructing decision models.

As the fuzzy set theory offers the possibility to model vague data as precise as a person can describes them, a lot of decision models with fuzzy components are proposed in literature since 1965. But in my opinion only fuzzy consequences and fuzzy probabilities are important for practical applications. Therefore, this paper is restricted to these subjects. It is shown that the decision models with fuzzy utilities or/and fuzzy probabilities are suitable for getting realistic models of real world decision situations. Moreover, we propose appropriate instruments for selecting the best alternative and for compiling a ranking of the alternatives. As fuzzy sets are not well ordered, this should be done in form of an interactive solution process, where additional information is gathered in correspondence with the requirements and under consideration of cost—benefit relations. This procedure leads to a reduction of information costs.