Since in many real-world applications using AHP,
the pairwise comparisons are subject to judgmental
errors and are inconsistent and conflicting with each
other, the weight point estimates provided by the
eigenvector method are necessarily approximates.
The uncertainty associated with subjective judgmental
errors may affect the rank order of decision
alternatives. A new stochastic approach, recently
introduced by Eskandari and Rabelo (2006), is
employed for handling the propagation of uncertainty
in the AHP and for capturing the uncertain
behavior of the global AHP weights. This approach
could help decision makers get insights into how the imprecision in judgment ratios may affect their
choice toward the best solution and how the best
alternative(s) may be identified with certain confidence
(Zahir, 1991). Moreover, the confidence of
decision makers is enhanced in the outcome of an
ensuing AHP synthesis (Rosenbloom, 1996