Group decision making with incomplete information: a dominance and quasi‐optimality volume‐based approach using Monte‐Carlo simulation

In this paper, we present a comprehensive framework for multiattribute group decision making considering that neither information about individual preferences nor the importance of individual decision makers for the group is available in exact form. We study several different forms of incomplete preference information, including a ranking of attribute weights, ranking of values of alternatives in each attribute, and ranking of value differences. Our approach is based on relative volumes in parameter space and allows for probabilistic statements about different results, including optimality or quasi‐optimality of alternatives, or relations between alternatives.