Robust estimation of priorities in the analytic hierarchy process

A pairwise comparison matrix of the Analytic Hierarchy Process (AHP) is considered as a contingency table that helps to identify unusual or false data elicited from a judge. Special techniques are suggested for robust estimation of priority vectors. They include transformation of a Saaty matrix to matrix of shares of preferences and solving an eigenproblem designed for the transformed matrices. We also introduce an optimizing objective that produces robust priority estimation. Numerical results are compared using the AHP with these differing approaches. The comparison demonstrates that robust estimations yield priority vectors not prone to influence of possible errors among the elements of a pairwise comparison matrix.