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Related Experiment Videos

Coherent probability from incoherent judgment.

D Osherson1, D Lane, P Hartley

  • 1Department of Psychology, Rice University, Mailstop 25, P.O. Box 1892, Houston, Texas 77251-1892, USA. osherson@rice.edu

Journal of Experimental Psychology. Applied
|October 2, 2001
PubMed
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This study developed an optimization method to correct incoherent probability judgments. The method improved the accuracy of chance estimates for meteorological events, making human judgment more useful for expert systems.

Area of Science:

  • Cognitive Science
  • Probability Theory
  • Meteorology

Background:

  • Individuals often possess knowledge about event likelihoods but struggle to articulate it as coherent probabilities.
  • Incoherent probability judgments can hinder decision-making processes and the development of expert systems.

Purpose of the Study:

  • To introduce and evaluate an optimization procedure for correcting incoherent probability judgments.
  • To assess the accuracy of these corrected probabilities in the context of meteorological event forecasting.

Main Methods:

  • An optimization procedure was designed to find the coherent probability distribution closest to a judge's initial estimates.
  • The method was applied to college undergraduates' estimations of simple and complex meteorological event probabilities.

Related Experiment Videos

  • The quadratic scoring rule was used to measure the accuracy of both original and optimized probability estimates.

    • Main Results:

    • No participant initially provided coherent probability estimates.
    • The optimization procedure successfully generated coherent approximations of the judges' estimates.
    • These coherent approximations demonstrated reliably higher accuracy compared to the original, incoherent estimates.

    Conclusions:

    • The proposed optimization method effectively corrects incoherent probability judgments.
    • Accurate probability estimation is crucial for expected utility analysis and expert system development.
    • This approach enhances the utility of human judgment in computational and decision-making frameworks.