Evaluate each alternative using the multiattribute

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Unformatted text preview: more detailed study. AHP assumes the existence of an underlying preference “Vector” (with magnitudes and directions) that is revealed through the pair-wise comparisons. This is a powerful assumption, which may at best hold only for the participating evaluators. The figure of merit produced for each alternative is the result of the group’s subjective judgments and is not necessarily a reproducible result. For information on AHP, see Thomas L. Saaty, The Analytic Hierarchy Process, 1980. NASA Systems Engineering Handbook Systems Analysis and Modeling Issues Multi-Attribute Utility Theory MAUT is a decision technique in which a figure of merit (or utility) is determined for each of several alternatives through a series of preference-revealing comparisons of simple lotteries. An abbreviated MAUT decision mechanism can be described in six steps: (1) (2) (3) (4) (5) (6) Choose a set of descriptive, but quantifiable, attributes designed to characterize each alternative. For each alternative under consideration, generate values for each attribute in the set; these may be point estimates, or probability distributions, if the uncertainty in attribute values warrants explicit treatment. Develop an attribute utility function for each attribute in the set. Attribute utility functions range from 0 to 1; the least desirable value, xi0, of an attribute (over its range of plausible values) is assigned a utility value of 0, and the most desirable, xi*, is assigned a utility value of 1. That is, ui(xi0) = 0 and ui(xi*) = 1. The utility value of an attribute value, xi, intermediate between the least desirable and most desirable is assessed by finding the value 0 xi such that the decision maker is indifferent between receiving xi for sure, or, a lottery that yields xi with 0 probability p i or xi* with probability 1 - pi. From the mathematics of MAUT, ui(xi) = pi ui(xi ) + (1 - pi) ui(xi*) = 1 - pi. Repeat the process of indifference revealing until there are enough discrete points to approximate a continuous attribute utility function. Combine the individual attribute utilit...
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This document was uploaded on 02/26/2014 for the course E 515 at University of Louisiana at Lafayette.

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