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Which Parameters Are Important? Differential Importance Under Uncertainty.

Isadora Antoniano-Villalobos1,2, Emanuele Borgonovo1,2, Sumeda Siriwardena3

  • 1Department of Decision Sciences, Bocconi University, Milan, Italy.

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This study introduces a new differential importance measure for probabilistic risk assessment. This method enhances sensitivity analysis by overcoming limitations of traditional approaches and improving uncertainty quantification.

Keywords:
Importance measuresrisk analysissensitivity analysisuncertainty analysis

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Area of Science:

  • Risk Assessment
  • Uncertainty Quantification
  • Computational Statistics

Background:

  • Probabilistic risk assessment (PRA) often focuses on expected risk metric values.
  • Sensitivity analysis is crucial for understanding input uncertainty but faces limitations with differentiation-based methods.
  • Existing methods struggle with parameters in different units, parameter grouping, and varying model levels.

Purpose of the Study:

  • To propose a novel approach for sensitivity analysis in PRA.
  • To address limitations of differentiation-based methods in handling parameter units, grouping, and model hierarchy.
  • To enable examination of the impact of input independence assumptions.

Main Methods:

  • Development and application of the differential importance measure.
  • Detailed discussion of estimation aspects, including single-sample Monte Carlo methods.
  • Illustration via an analytical example and a case study on the Advanced Test Reactor (ATR) PRA.

Main Results:

  • The differential importance measure overcomes limitations of traditional sensitivity analysis methods.
  • The approach allows for unified sensitivity insights across different parameter units and model levels.
  • Efficient estimation from a single Monte Carlo sample is demonstrated, reducing computational cost.
  • The method effectively assesses the impact of removing independence assumptions between inputs.

Conclusions:

  • The differential importance measure offers a robust and versatile tool for PRA sensitivity analysis.
  • This method provides deeper insights into uncertainty propagation and parameter importance.
  • The approach is applicable to complex PRA models, such as the ATR large loss of coolant accident sequence.