Probabilistic inversion of expert judments in the quantification of model uncertainty

BCP Kraan, TJ Bedford

    Research output: Contribution to journalArticleScientific

    34 Citations (Scopus)

    Abstract

    Expert judgment is frequently used to assess parameter values of quantitative management science models, particularly in decision-making contexts. Experts can, however, only be expected to assess observable quantities, not abstract model parameters. This means that we need a method for translating expert assessed uncertainties on model outputs into uncertainties on model parameter values. This process is called probabilistic inversion. The probability distribution on model parameters obtained in this way can be used in a variety of ways, but in particular in an uncertainty analysis or as a Bayes prior. This paper discusses computational algorithms that have proven successful in various projects and gives examples from environmental modelling and banking. Those algorithms are given a theoretical basis by adopting a minimum information approach to modelling partial information. The role of minimum information is two-fold: It enables us to resolve the problem of nonuniqueness of distributions given the information we have, and it provides numerical stability to the algorithm by guaranteeing convergence properties. Key words: multivariate distribution; uncertainty analysis; expert judgment; probabilistic inversion; relative information; minimum entropy; interior point method; credit scoring; environmental modeling
    Original languageUndefined/Unknown
    Pages (from-to)995-1006
    Number of pages12
    JournalManagement Science
    Volume51
    Issue number6
    Publication statusPublished - 2005

    Keywords

    • academic journal papers
    • ZX CWTS 1.00 <= JFIS < 3.00

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