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Methods for combining experts' probability assessments

R A Jacobs1

  • 1Department of Brain and Cognitive Sciences, University of Rochester, NY 14627, USA.

Neural Computation
|September 1, 1995
PubMed
Summary
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This study reviews methods for combining expert probability distributions, focusing on supra-Bayesian and linear opinion pool techniques. Addressing expert opinion dependence is crucial for accurate decision-making aggregation.

Area of Science:

  • Statistics
  • Decision Theory

Background:

  • Decision makers often consult multiple experts for event probabilities.
  • Experts provide opinions as probability distributions.
  • Aggregating diverse expert opinions into a single, usable distribution is challenging.

Purpose of the Study:

  • To review statistical techniques for combining multiple probability distributions from experts.
  • To analyze the impact of dependence among expert opinions on aggregation methods.
  • To explore strategies for improving the reliability of aggregated expert judgments.

Main Methods:

  • Review of supra-Bayesian procedures, treating expert opinions as data combined via Bayes' rule.
  • Review of linear opinion pools, forming linear combinations of expert probability distributions.

Related Experiment Videos

  • Analysis of the impact of correlated expert opinions on aggregation effectiveness.

    • Main Results:

    • Dependent expert opinions significantly reduce the value of aggregated information compared to independent opinions.
    • The equivalence of 'm' dependent experts to 'k' independent experts (where k ≤ m) is demonstrated.
    • Methods for quantifying the reduction in value due to dependence, including bounds for 'k', are presented.

    Conclusions:

    • The dependence among expert opinions is a major challenge in aggregation.
    • Training experts for independent opinions or using aggregation methods that model dependence is essential.
    • Understanding the impact of dependence allows for more accurate assessment of expert-derived information value.