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Related Experiment Video

Updated: Jul 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A method for evaluating elicitation schemes for probabilistic models.

H Wang¹, D Dash, M J Druzdzel

¹Decision Syst. Lab., Pittsburgh Univ., PA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

This study introduces an objective method to evaluate how experts provide their beliefs for probabilistic models. The scaled probability bar proved most effective for eliciting discrete probabilities in our experiment.

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Published on: September 19, 2012

Area of Science:

Decision Analysis
Cognitive Science
Probabilistic Modeling

Background:

Evaluating the accuracy of expert-elicitation methods is crucial for building reliable probabilistic models.
Traditional methods often lack objective standards for comparison, relying on subjective assessments.
Understanding how experts translate their knowledge into quantifiable probabilities is a key challenge.

Purpose of the Study:

To propose an objective framework for assessing probability and structure elicitation techniques.
To establish a methodology for comparing different expert elicitation methods using a derived model as a benchmark.
To identify the most effective method for eliciting discrete probabilities from experts.

Main Methods:

Developed a general procedure to capture expert beliefs and form a derived model.
Used the derived model as a standard against which elicited models are compared.
Conducted an experiment comparing three discrete probability elicitation methods: direct numerical assessment, probability wheel, and scaled probability bar.

Main Results:

The scaled probability bar demonstrated superior performance in eliciting discrete probabilities compared to direct numerical assessment and the probability wheel.
The objective approach allowed for a quantitative comparison of the effectiveness of different elicitation tools.
The derived model served as a reliable benchmark for evaluating elicited probabilities.

Conclusions:

The scaled probability bar is the most effective tool for eliciting discrete probabilities in the domain studied.
The proposed objective approach provides a robust framework for evaluating and improving expert elicitation methods.
This research contributes to more accurate and reliable probabilistic modeling through enhanced expert knowledge integration.