Decision-theoretic reliability sensitivity

TL;DR

This paper introduces decision-theoretic sensitivity metrics for reliability analysis based on information value, offering easier interpretation and broad applicability, with efficient computational strategies demonstrated through numerical examples.

Contribution

It presents novel sensitivity metrics rooted in decision theory that are easier to interpret and applicable to various reliability assessments, including dependent inputs.

Findings

Metrics are easier to interpret than existing ones.

Applicable to any reliability assessment, including dependent inputs.

Efficient computational strategies enable practical evaluation.

Abstract

We propose and discuss sensitivity metrics for reliability analysis, which are based on the value of information. These metrics are easier to interpret than other existing sensitivity metrics in the context of a specific decision and they are applicable to any type of reliability assessment, including those with dependent inputs. We develop computational strategies that enable efficient evaluation of these metrics, in some scenarios without additional runs of the deterministic model. The metrics are investigated by application to numerical examples.