Sensitivity of multiattribute decision models to alternative specifications of weighting parameters

The issue of attribute weighting in multiattribute decision models is examined. Results are presented which show that the outputs produced by linear multiattribute models are extremely robust with respect to alternative specifications of the weighting parameters unless the number of attributes included in the models is small, the average correlation among the attributes is low, and the dispersion of the weights is large relative to their mean. Implications of these results are discussed for three different types of weighting schemes-regression weighting, equal weighting, and subjective weighting—which are used in multiattribute decision modeling.