This paper uses a simulation approach to investigate how different attribute weightingtechniques affect the quality of decisions based on multiattribute value models. The weightingmethods considered include equal weighting of all attributes, two methods for using judgmentsabout the rank ordering of weights, and a method for using judgments about the ratios of weights.The question addressed is: How well does each method perform when based on judgments ofattribute weights that are unbiased but subject to random error? To address this question, weemploy simulation methods. The simulation results indicate that ratio weights were either betterthan rank order weights (when error in the ratio weights was small or moderate) or tied with them(when error was large). Both ratio weights and rank order weights were substantially superior tothe equal weights method in all cases studied. Our findings suggest that it will usually be worththe extra time and effort required to assess ratio weights. In cases where the extra time or effortrequired is too great, rank order weights will usually give a good approximation to the trueweights. Comparisons of the two rank-order weighting methods favored the rank-order-centroidmethod over the rank-sum method.