Towards secure judgments aggregation in AHP
This addresses security issues in group decision-making for domains like business or policy, but it is incremental as it builds on existing GAHP frameworks.
This paper tackles the problem of dishonest experts manipulating group decisions in the Group Analytic Hierarchy Process (GAHP) by introducing two heuristics to detect manipulators and reduce their influence on consensus through weight adjustment. The methods are demonstrated with numerical examples and simulations, though no concrete performance numbers are provided.
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 group decision making framework, such as the group analytic hierarchy process (GAHP), try to manipulate results in their favor. The aim of this paper is to introduce two heuristics in the GAHP, setting allowing to detect the manipulators and minimize their effect on the group consensus by diminishing their weights. The first heuristic is based on the assumption that manipulators will provide judgments which can be considered outliers with respect to those of the rest of the 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.