Abstract
Expected utility theory is by far the best normative theory for decision making under uncertainty. It helps the decision maker find the proper balance between expected profits and risks, and has been acknowledged as a key approach to rational economic behavior of individuals. The whole measurement process in the expected utility theory is based on the solution of preferential equations, with the help of which utilities and probabilities are being elicited. However, the resulting estimates are in an interval form, which disobeys some main rationality assumptions of the theory. Therefore, fuzzy rational decision analysis in introduced as a way to unify the normative rationality with the fuzziness of real preferences. This chapter outlines a series of practical techniques dealing with the interval nature of assessed utility and probability measures, using the intrinsic optimism-pessimism attitude of the DM. Main preference-related and uncertainty-related problems are stressed.
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Nikolova, N.D., Tenekedjiev, K. (2014). Fuzzy Rationality Implementation in Financial Decision Making. In: Espin, R., Pérez, R., Cobo, A., Marx, J., Valdés, A. (eds) Soft Computing for Business Intelligence. Studies in Computational Intelligence, vol 537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53737-0_23
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DOI: https://doi.org/10.1007/978-3-642-53737-0_23
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