Shorter prediction intervals for anonymous individual assessments in group decision-making via pairwise comparisons

With reference to pairwise comparisons in group decision-making, consider the problem of how a decision-maker, who knows his/her own assessment and the aggregate assessment of all decision-makers, can predict the assessments of the other decision-makers. We address this problem from a statistical perspective and propose statistical prediction intervals. These are seen to be often significantly shorter and hence appreciably more powerful than their deterministic counterparts available in the literature, accounting for the confidence level as well. In the process, analytical closed form solutions for these deterministic intervals are also obtained. Extensive computations and simulations show that our statistical approach remains very robust to departure from underlying assumptions, frequently being even more efficient. Moreover, this approach is found to be useful generally in predicting an unknown individual component of a given total, thus having much wider applicability beyond the immediate context of pairwise comparisons in group decision-making.