Analysis of Correlated Expert Judgments from Extended Pairwise Comparisons

Jason R. W. Merrick (), J. Rene van Dorp () and Amita Singh ()
Additional contact information
Jason R. W. Merrick: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, P.O. Box 843083, 1001 West Main Street, Richmond, Virginia 23284
J. Rene van Dorp: George Washington University, 1776 G. Street, NW, Washington, DC 20052
Amita Singh: George Washington University, 1776 G. Street, NW, Washington, DC 20052

Decision Analysis, 2005, vol. 2, issue 1, 17-29

Abstract: We develop a Bayesian multivariate analysis of expert judgment elicited using an extended form of pairwise comparisons. The method can be used to estimate the effect of multiple factors on the probability of an event and can be applied in risk analysis and other decision problems. The model, which parallels Bayesian models for combining expert judgments, provides predictions of the quantity of interest that incorporate dependencies among the various experts. In this form we may learn about the dependencies between the experts from their responses. The analysis is applied to a real data set of expert judgments elicited during the Washington State Ferries Risk Assessment. The effect of the statistical dependence among experts is compared to an analysis assuming independence among them.

Keywords: expert judgment; pairwise comparisons; Bayesian statistics; multivariate analysis (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:2:y:2005:i:1:p:17-29

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