Multiattribute utility models are used for evaluating alternatives when there are more than one criterion present. There is a trend toward the development of complicated versions of these models. These versions, although theoretically more accurate in the representation of decision makers' attitudes, require assessment procedures which are more difficult and time consuming to implement than simpler models. This paper reviews theoretical and empirical research involving the sensitivity analysis of multiattribute utility models in an attempt to answer the question of whether such additional complexities are worthwhile. Both deterministic and probabilistic models are considered and the studies are divided into four areas: (1) those involving sensitivity to the form of the multiattribute utility function; (2) those involving sensitivity to the parameters of the functions; (3) those involving sensitivity to the form and parameters of individual single attribute utility functions; and (4) those involving the relationship between deterministic and probabilistic models. A discussion of the results is given at the end.
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P. K. Leung (1978) studied this question.
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