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Managing uncertainty in probabilistic risk analysis (PRA) means making clear what is random, what is uncertain about the model, how either could affect a decision, and what evidence would let another analyst review or rerun the work. A PRA cannot eliminate uncertainty. It can make uncertainty visible, test whether it matters to the decision, and preserve a defensible record of how results were produced.
Separate randomness from uncertainty about the model
Start by distinguishing two different questions: what variation is part of the process being analyzed, and what is uncertain because analysts do not know enough about the process or its representation.
- Aleatory uncertainty is variability associated with the randomness of events or outcomes represented within the PRA.
- Epistemic uncertainty is uncertainty about the PRA formulation. The U.S. Nuclear Regulatory Commission (NRC) identifies parameter, model, and completeness uncertainty as epistemic categories.
This distinction matters because the remedies differ. More simulation may characterize represented variability, but it does not by itself resolve uncertainty about parameters, model structure, or omitted events. The NRC discusses these categories in NUREG-1855, Revision 1, which addresses uncertainty in PRA for risk-informed decisionmaking.
Connect the analysis to the decision it must support
Before selecting methods or interpreting a result, state the decision, the model’s intended use, and the application-specific acceptance guidelines. A numerical result has no decision meaning on its own: its relevance depends on the question being asked and the criteria used to judge the outcome.
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Consider whether uncertainty or incompleteness could change the decision. If a conclusion depends on a narrow assumption, a model boundary, or a source of uncertainty that is not well characterized, make that dependency visible rather than presenting the result as unconditional. Where the application calls for it, connect the analysis to monitoring, feedback, and corrective action so evidence gathered after the decision can inform future use.
The NRC’s guidance frames uncertainty treatment in the context of risk-informed decisions. It is not a universal set of acceptance thresholds for every sector; use the criteria and governing rules that apply to the specific application.
Check implementation, real-world fit, and uncertainty separately
Three credibility checks answer different questions. Treating them as interchangeable can leave a model with correct code but poor real-world fit, or a plausible model whose implementation has not been checked.
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Verification: does the implementation match its mathematical description?
Verification examines whether the computational implementation is consistent with the model’s mathematical specification. Keep evidence of the checks performed, their scope, and any issues found and resolved.
Validation: does the model represent its intended application well enough?
Validation assesses how well the model represents the real-world system or application for which it is intended. Evidence should be judged in that context; validation for one use does not automatically establish fitness for a different use.
Uncertainty quantification: how do uncertain inputs affect outcomes?
Uncertainty quantification assesses how variation in parameters affects results. It helps show which input uncertainty matters to the outcome, but it does not replace verification or validation. ASME distinguishes these activities in its overview of verification, validation, and uncertainty quantification (VVUQ). The overview describes computational-modeling guidance, not a complete PRA audit-trail framework.
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Use Monte Carlo methods transparently
Monte Carlo analysis can be a viable way to analyze variability and uncertainty when it is supported by adequate data and credible assumptions. The U.S. Environmental Protection Agency (EPA) identifies clarity, consistency, transparency, reproducibility, and sound methods as good scientific practices. Its Guiding Principles for Monte Carlo Analysis dates to March 1997, so it is foundational guidance rather than evidence that every current sector uses one identical protocol.
For a Monte Carlo PRA, make the choices that shape the result inspectable: document the input data and assumptions, how parameters were represented, the computational method and run configuration, and how outputs were interpreted against the decision criteria. Preserve intermediate results for nondeterministic steps when they cannot be regenerated. A reproducible run is useful only if a reviewer can also understand what the model represents and why its assumptions are credible.
Build an audit trail that supports review and reruns
A useful audit trail lets an independent reviewer identify what decision the analysis supported, understand how the results were produced, examine the checks performed, and reproduce the computation where feasible. The National Academies recommends conveying computational methods and data products clearly and completely so others can repeat an analysis, subject to restrictions such as nonpublic data policies. Its examples include input data, intermediate results for nondeterministic steps, methods and parameters, and the original computational environment, including operating system, hardware architecture, and dependencies.
The following record is a practical synthesis of the National Academies, EPA, NRC, and Federal Reserve materials—not a universal schema prescribed by any one of them. Tailor it to the application, decision criteria, applicable rules, and model-risk controls.
- Decision and use: the decision supported, intended model use, relevant criteria, and limitations.
- Data and assumptions: input data, provenance, assumptions, parameter choices, and known gaps.
- Model and method: model formulation, computational methods, and relevant version information.
- Run details: code, dependencies, parameters, configuration, and computational environment, such as the operating system and hardware architecture.
- Outputs needed to reproduce the work: input and final results, plus intermediate or nondeterministic outputs that cannot be regenerated.
- Credibility evidence: verification and validation evidence, along with the uncertainty analysis and how its results were interpreted against decision criteria.
- Changes after deployment or initial use: relevant monitoring, feedback, and corrective actions.
The National Academies’ Recommendation 4-1 says researchers should convey clear, specific, and complete information about computational methods and data products so others can repeat the analysis, unless nonpublic-data policies restrict that information. In practice, a rerun also depends on preserving enough context to interpret the output, not just the executable code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare analyses and model versions on decision-relevant evidence
When reviewing a new analysis or a model update, compare the evidence behind the decision rather than treating a changed output as self-explanatory.
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| Comparison area | What to examine |
|---|---|
| Uncertainty scope | Which variability and epistemic sources are represented, and whether any material source or potential incompleteness is outside the analysis. |
| Data and assumptions | Input provenance, assumptions, and changes in parameter choices. |
| Verification | Evidence that the implementation matches its mathematical description. |
| Validation | Evidence that the model fits the intended application. |
| Reproducibility | Whether the computational environment, methods, inputs, and necessary outputs are preserved well enough to rerun or review the analysis. |
| Decision sensitivity | Whether conclusions or acceptance judgments change under the uncertainty considered. |
| Ongoing governance | Monitoring, feedback, and corrective-action plans relevant to continued use. |
These comparison areas synthesize the NRC, ASME, and National Academies guidance; they are not a single prescribed checklist.
Govern the model as its use changes
A model’s credibility is tied to its intended use. Using it beyond that purpose can introduce additional uncertainty and risk, so changes in application should prompt a fresh look at limitations and performance. The Federal Reserve’s supervisory guidance, published April 17, 2026, makes this point in the context of banking organizations and calls for understanding limitations and ongoing performance assessment. It is banking-sector supervisory guidance, not a universal legal requirement for all PRA users.
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