Uncertainty assessment in validating a risk study

Nazmul Rahmani

doi:10.1016/j.psep.2026.108909

Uncertainty assessment in validating a risk study

2026-04-21

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DOI：10.1016/j.psep.2026.108909

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Risk assessment outcomes inherently carry a degree of uncertainty. This uncertainty arises from several factors, such as incomplete evidence, overlooked hazards, insufficient or non-existent data, and the inherent limitations of analytical models. This raises a critical question: How much confidence can decision-makers reasonably have in these results? Traditionally, risk communication has often relied on single-point estimates, yet presenting only such values fails to capture the full scope of uncertainty. For decision-makers, risk assessments are essential but not the sole basis for decisions. Recognizing the range of possible risk outcomes through confidence intervals is crucial. Depending on the context and other influencing factors, a decision-maker might prefer to consider either an average risk value or a probability distribution that reflects the inherent uncertainty in the risk estimates. This paper explores the diverse application areas of risk assessment and emphasizes the importance of these analyses. It then introduces a validation procedure aimed at bolstering confidence in risk outcomes. This work argues that reporting confidence limits is essential for robust decision-making. It reviews the current state of validation in risk studies, highlighting the "maturity model" for quantitative risk assessment (QRA) and proposing strict procedural improvements—such as the separation of analyst and decision-maker, the application of system theory, and the mandatory use of sensitivity analyses—to enhance the reliability of risk-informed decisions. It proposes a decision-oriented approach featuring a new operational tool – a QRA “credit score”, AI-assisted completeness checks for hazard identification, interactive confidence-interval dashboards, and double-blind assessments. An illustrative example demonstrates how these tools can be used to judge when a risk study is fit or, in contrast, unfit for decision-making.

Keywords:

Risk assessment

Uncertainty quantification

Validation

Confidence intervals

Decision-making

Uncertainty assessment in validating a risk study

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