Article ID: | iaor2009533 |
Country: | United States |
Volume: | 34 |
Issue: | 3 |
Start Page Number: | 443 |
End Page Number: | 466 |
Publication Date: | Jul 2004 |
Journal: | Decision Sciences |
Authors: | Hahn Eugene D. |
Keywords: | decision theory: multiple criteria, markov processes |
In the analytic hierarchy process (AHP), priorities are derived via a deterministic method, the eigenvalue decomposition. However, judgments may be subject to error. A stochastic characterization of the pairwise comparison judgment task is provided and statistical models are introduced for deriving the underlying priorities. Specifically, a weighted hierarchical multinomial logit model is used to obtain the priorities. Inference is then conducted from the Bayesian viewpoint using Markov chain Monte Carlo methods.