In the context of additive multiattribute aggregation,we address problems with ordinal information, i.e., consideringa ranking of the weights (the scaling coefficients). Several rulesfor ranking alternatives in these situations have been proposedand compared, such as the rank-order-centroid weight, minimumvalue, central value, and maximum regret rules. This paper comparesthese rules, together with two rules that had never been studied(quasi-dominance and quasi-optimality) that use a toleranceparameter to extend the concepts of dominance and optimality.Another contribution of this paper is the study of the behaviorof these rules in the context of selecting a subset of the mostpromising alternatives. This study intends to provide guidelinesabout which rules to choose and how to use them (e.g., how manyalternatives to retain and what tolerance to use), considering thecontradictory goals of keeping a low number of alternatives yet notexcluding the best one. The comparisons are grounded on MonteCarlo simulations.
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