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Using probability boxes to model elicited information: a case study.

V J Roelofs1, W Roelofs

  • 1The Food and Environment Research Agency, Risk and Numerical Science Team, Sand Hutton, York, YO41 1LZ, UK.

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|December 13, 2012
PubMed
Summary
This summary is machine-generated.

Probability boxes (p-boxes) offer a flexible method for analyzing expert-elicited information, improving uncertainty representation in policy decisions. This approach avoids rigid distributional assumptions, enhancing the reliability of model-based analyses when data is limited.

Keywords:
Dependenciesexpert elicitationprobability boxes

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Area of Science:

  • Decision Science
  • Risk Analysis
  • Mathematical Modeling

Background:

  • Government policy decisions frequently involve significant uncertainty due to incomplete information.
  • Expert elicitation is a valuable tool for addressing data gaps in situations with time or resource constraints.
  • Traditional modeling often requires assumptions about variable distributions and dependencies, which may not accurately reflect expert knowledge.

Purpose of the Study:

  • To demonstrate the utility of probability boxes (p-boxes) for enhancing uncertainty representation in analyses derived from expert elicitation.
  • To explore how p-boxes can accommodate elicited quantiles and interval data without imposing restrictive distributional assumptions.
  • To illustrate methods for modeling elicited quantiles using both nonparametric and parametric p-boxes, even when distribution shapes are uncertain or mismatched.

Main Methods:

  • Utilized probability boxes (p-boxes) as a flexible methodology for uncertainty analysis.
  • Focused on modeling elicited quantiles and interval information using nonparametric p-boxes.
  • Investigated modeling elicited quantiles with parametric p-boxes, addressing potential mismatches between elicited quantiles and assumed distribution shapes.

Main Results:

  • P-boxes provide bounds on model outputs by considering possible input distributions without assuming a specific shape.
  • This methodology allows for the combination of variables without requiring explicit dependence assumptions.
  • The approach effectively represents uncertainty when dealing with elicited quantiles and interval data.

Conclusions:

  • Probability boxes significantly improve the representation of uncertainty in analyses based on expert elicitation.
  • P-boxes offer a robust alternative to traditional modeling assumptions, particularly when dealing with limited or uncertain data.
  • The application of p-boxes enhances the reliability and flexibility of quantitative analyses supporting policy decisions under uncertainty.