abstract
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A fuzzy decision making model is proposed to support decision making under uncertainty. This model incorporates three theories and methodologies: classical decision making theory under conflict, as suggested by Luce and Raiffa (1957), the fuzzy set theory of Zadeh (1965, 1984), and a modified version of the back propagation neural network algorithm originated by Rumelhart et al. (1986). An algorithm which implements the model is also described.