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Supporting Consumer Decision by Fuzzy Revealed Preference in Online Sales

This paper deals with a model of decision– making in two stages. In the first stage from a set of expertises one chooses the most rational ones, while in the second stage one obtains a ranking of alternatives according to different criteria. The mathematical basis of the model is the revealed preference theory for fuzzy choice functions. With them one can represent the situation when in the choice of the best options vague criteria or attributes appear. Two notions associated with a fuzzy choice function are studied: the normality coefficient used to determine the most rational expertise and the degree of dominance used in the multicriterial ranking of alternatives. Different from other possible solutions, our model tries to correct market information asymmetry between a consumer and several producers. By interpreting the vectors that contain certain information as criteria for the customer in decision–making and by combining it with the certain information existing in the model, an optimal suggestion was illustrated by revealed preference theory and could be regarded as a recommendation for a more reliable purchase decision.