Abstract
AbstractA powerful application of decision theory to engineering problems often has failed: The uncertainty underlying is too complex to be modelled adequately by a (precise) probability distribution. The present paper shows how recent generalizations of the usual calculus of probability can be utilized to deal powerfully with complex uncertainty in decision problems. Basic notions of the resulting theory of generalized expected loss and generalized risk are developed and discussed. In addition to this, also a general applicable algorithm is proposed to calculate optimal decision functions by linear programming.
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Schedule a callMore From: ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik
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