Resilient heuristic aggregation of judgments in the pairwise comparisons method

Abstract

In decision-making methods, it is common to assume that the experts are honest and professional. However, this is not the case when one or more experts in the pairwise-based group decision-making framework, such as the group analytic hierarchy process, try to manipulate results in their favor. This paper aims to introduce two heuristics enabling detection of manipulators and minimizing their effect on the group consensus by diminishing their weights. The first heuristic is based on the assumption that manipulators will provide judgments that can be considered outliers with respect to those of the other experts in the group. The second heuristic assumes that dishonest judgments are less consistent than the average consistency of the group. Both approaches are illustrated with numerical examples and simulations.