Sensitivity analysis of the effect of variations in the form and parameters of a multiattribute utility model: A survey

Abstract

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.KEY WORDS: multiattribute utility models, sensitivity analysis, vmation in form and parameters, deterministic and probabilistic models, decision making. bu PURPOSE AND SCOPEHIS PAPER gives a survey of theoretical T and empirical work on the sensitivity analysis of the effect of variations in the form and parameters of multiattribute utility models. Multiattribute utility models are used by decision analysts to evaluate alternatives or items that have more than one factor of interest. The main goal is to determine the desirability of an alternative or item by decomposing it into its component factors or attributes; evaluating the worth or utility of each attribute; and combining them into an overall utility for the alternative or item. Models are deterministic when no uncertainty about the outcome of each alternative is involved. They are probabilistic when risk is involved (von Winterfelt, 1975).Strictly speaking, the form and parameters of the model should reflect the attitudes of the decision maker and, in general,

Attainable bounds for generalized moments via mathematical programming

“…An example of this is in the selection of safety programs for nuclear reactor safety as described by Ritzman and Husseiny (1980). Not everyone is enthusiastic about this extension, however, as for example Leung (1978) who questions the worth of the additional complexity required. This present work will not delve into these multi-attribute issues, and will be restricted to what is commonly referred to as univariate utility theory.…”