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Updated: Jun 12, 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

Ignorance is not probability.

William A Huber¹

¹Quantitative Decisions, 1235 Wendover Road, Rosemont, PA 19010, USA. whuber@quantdec.com

Risk Analysis : an Official Publication of the Society for Risk Analysis

|May 22, 2010

Summary

This summary is machine-generated.

Understanding the difference between parameter ignorance and probability distributions is crucial for accurate risk assessment. This distinction significantly impacts decision-making and the evaluation of objective risk analysis methods.

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Last Updated: Jun 12, 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

Area of Science:

Decision Analysis
Risk Assessment
Probability Theory

Background:

Distinguishing between complete ignorance and probabilistic knowledge of parameters is vital in risk assessment.
This distinction has tangible consequences for decision-making processes.
Previous objective methods for risk analysis have faced criticism.

Purpose of the Study:

To highlight the critical difference between parameter ignorance and probabilistic knowledge in risk assessment.
To illustrate the practical implications of this distinction through hypothetical dialogs.
To re-evaluate nonprobabilistic, objective risk assessment methods within a decision-theoretic framework.

Main Methods:

Illustrative dialogs between a decision-maker and a risk assessor.
Exposition of risk analysis within a decision-theoretic framework.
Analysis of Terje Aven's criticism of nonprobabilistic methods.

Main Results:

The distinction between ignorance and probabilistic knowledge has significant real-world consequences.
A decision-theoretic framework clarifies the importance of this distinction.
Nonprobabilistic objective methods may be less effective than probabilistic approaches.

Conclusions:

Emphasizing the difference between ignorance and probabilistic knowledge enhances risk assessment accuracy.
A decision-theoretic approach provides a robust framework for evaluating risk assessment methods.
More effective evaluation strategies for objective risk methods are needed.