A Numerical Comparison of the Sensitivity of the Geometric Mean Method, Eigenvalue Method, and Best-Worst Method

In this paper, we compare three methods for deriving a priority vector in the theoretical framework of pairwise comparisons-the Geometric Mean Method (GMM), Eigenvalue Method (EVM) and Best-Worst Method (BWM)-with respect to two features: sensitivity and order violation. As the research method, we apply One-Factor-At-a-Time (OFAT) sensitivity analysis via Monte Carlo simulations; the number of compared objects ranges from 3 to 8, and the comparison scale coincides with Saaty's fundamental scale from 1 to 9 with reciprocals. Our findings suggest that the BWM is, on average, significantly more sensitive statistically (and thus less robust) and more susceptible to order violation than the GMM and EVM for every examined matrix (vector) size, even after adjustment for the different numbers of pairwise comparisons required by each method. On the other hand, differences in sensitivity and order violation between the GMM and EMM were found to be mostly statistically insignificant.

Automated summary

The study indicates that the Best-Worst Method is statistically more sensitive and prone to order violation compared to the Geometric Mean Method and Eigenvalue Method. However, the differences in sensitivity and order violation between the Geometric Mean Method and Eigenvalue Method are mostly statistically insignificant.