Judgmental consistency and consensus in stochastic multicriteria decision making

Stochastic multicriteria decision making methods are a class of multicriteria decision making methods in which judgments are not taken to be certain. Recently, this class of models has generated increasing interest in the literature. We unify the notions of judgmental consistency and intra-group consensus under a framework of preference homogeneity in the context of this class of models. We propose new methods and measures for examining departures from consistency by using the Kullback–Leibler divergence. We also propose a new method, the jackknifed Kullback–Leibler divergence, that characterizes the extent to which a group member exhibits preference consensus with the remaining members of the group.