Decision making with uncertain judgments: a stochastic formulation of the analytic hierarchy process

Decision making with uncertain judgments: a stochastic formulation of the analytic hierarchy process

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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:
Keywords: decision theory: multiple criteria, markov processes
Abstract:

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.

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