A Simple Multiattribute Utility Procedure for Evaluation

The general problem of how to determine the worth or utility of alternatives that vary on many dimensions is of great practical importance. Although the number and types of situations that require such evaluations are large, the most usual way of performing such tasks has been unaided “intuition” (or, clinical judgment); i.e., the decision maker somehow does a mental trade-off analysis between the various attributes and alternatives in order to come to an evaluation/ decision. The cognitive difficulties of performing such a feat are formidable. For example, consider a situation with ten alternatives, each varying on six attributes. The intuitive decision maker has the task of locating ten alternatives in a six dimensional indifference space and picking the one with the highest utility. In such complex situations, an accumulating body of psychological research on the decision process has shown that people will reduce task complexity by using various heuristics (e.g., Tversky, 1969; 1972; Payne, 1976). While these heuristics have the advantage of allowing a decision maker to perform a complex task, they may lead to non-optimal behavior (e.g., consistent intransitivities). Furthermore, the literature on clinical judgment (Meehl, 1954; Sawyer, 1966) has also shown that experts have great difficulty in intuitively combining information in appropriate ways.

[1]  K. R. Hammond,et al.  Science, values, and human judgment. , 1976, Science.

[2]  John W. Payne,et al.  Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .

[3]  Howard Wainer,et al.  Estimating Coefficients in Linear Models: It Don't Make No Nevermind , 1976 .

[4]  R. Hogarth,et al.  Unit weighting schemes for decision making , 1975 .

[5]  Ward Edwards,et al.  1 – PUBLIC VALUES: MULTIATTRIBUTE-UTILITY MEASUREMENT FOR SOCIAL DECISION MAKING , 1975 .

[6]  M. Kaplan,et al.  Human judgment and decision processes , 1975 .

[7]  H. J. Einhorn Expert judgment: Some necessary conditions and an example. , 1974 .

[8]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[9]  George P. Huber,et al.  Multi-Attribute Utility Models: A Review of Field and Field-Like Studies , 1974 .

[10]  R. Dawes,et al.  Linear models in decision making. , 1974 .

[11]  A. Tversky Elimination by aspects: A theory of choice. , 1972 .

[12]  Hillel J. Einhorn,et al.  Expert measurement and mechanical combination , 1972 .

[13]  A. Tversky Intransitivity of preferences. , 1969 .

[14]  Jacob Cohen Multiple regression as a general data-analytic system. , 1968 .

[15]  Ward Edwards,et al.  Probabilistic Information Processing Systems: Design and Evaluation , 1968, IEEE Trans. Syst. Sci. Cybern..

[16]  J. Sawyer,et al.  Measurement and prediction, clinical and statistical. , 1966, Psychological bulletin.

[17]  Robert T. Eckenrode,et al.  Weighting Multiple Criteria , 1965 .

[18]  E. Ghiselli Theory of psychological measurement , 1964 .

[19]  D. B. Yntema,et al.  Man-Computer Cooperation in Decisions Requiring Common Sense , 1961 .

[20]  P. Meehl,et al.  Clinical versus Statistical Prediction. , 1955 .