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TESTING THE ROBUSTNESS OF MULTIATTRIBUTE UTILITY THEORY IN AN APPLIED SETTING *
Author(s) -
Farmer Timothy A.
Publication year - 1987
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1987.tb01515.x
Subject(s) - computer science , unitary state , robustness (evolution) , independence (probability theory) , conditional independence , expected utility hypothesis , audit , artificial intelligence , machine learning , mathematical optimization , mathematics , mathematical economics , statistics , biochemistry , chemistry , management , political science , law , economics , gene
Multiattribute utility theory (MAUT) was employed to model the professional judgments of external auditors. Fully developed MAUT models elicited from each subject according to keeney and Raiffa's [6] procedures were used to predict the internal control systems evaluations made by auditor‐subjects. Correlation analyses were used to compare the predictive ability of the “correct” MAUT models to the accuracy of models developed under simplifications of the MAUT procedures. One simplified model resulted from relaxing the requirements for attribute independence that determine the functional forms. A second modified MAUT function was formed using unitary weightings on conditional utility functions instead of elicited scaling constants. Tests showed essentially no significant differences in predictive accuracy among the models in the contact of this study.