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Probability Elicitation Under Severe Time Pressure: A Rank-Based Method.

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  • 1Munich School of Management, Ludwig-Maximilians-Universität of Munich, Schackstr. 4, 80539, Munich, Germany.

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Summary
This summary is machine-generated.

This study introduces a fast, non-numerical probability elicitation method using ordinal ranking. This approach approximates probabilities efficiently, aiding risk analysis for complex events.

Keywords:
Emerging threatsmaximum entropyordinal informationprobabilistic risk analysisprobability elicitation

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Area of Science:

  • Decision Analysis
  • Risk Assessment
  • Subjective Probability

Background:

  • Traditional probability elicitation methods prioritize precision over speed.
  • Existing numerical methods face expert refusal due to uncertainty or lack of evidence.
  • Rapid assessment is crucial for experts analyzing numerous events frequently.

Purpose of the Study:

  • To develop a fast and non-numerical probability elicitation protocol.
  • To address limitations of existing numerical and speed-neglecting methods.
  • To enable probability assessment for challenging event types.

Main Methods:

  • Utilized an ordinal ranking approach from multicriteria decision analysis.
  • Employed a maximum entropy principle algorithm to approximate probabilities from rankings.
  • Tested accuracy via Monte Carlo simulation and practical application.

Main Results:

  • The ordinal ranking method provides a fast and non-numerical alternative for probability elicitation.
  • Approximated probabilities demonstrated accuracy in simulations.
  • Successfully applied to a real-world animal health threat prioritization for DEFRA.

Conclusions:

  • The proposed method offers a practical solution for efficient subjective probability assessment.
  • It is suitable for diverse event types, including uncertain and politicized topics.
  • Enhances risk and decision analysis by incorporating expert judgment more effectively.