GCRI · Mathematics, statistics and data
Uncertainty quantification and model validation
Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model’s outputs deserve. Systems Science and Modelling stewards this page for the Nexus Consortium.
01Contents
Uncertainty quantification and model validation in six parts.
In this dossier6 parts
- 01The methodWhat uncertainty quantification and model validation covers
- 02Reference practiceUncertainty quantification follows verification and validation practice
- 03In systemic risk workUncertainty that survives the chain
- 04Applied inPlatforms that apply this method
- 05StewardshipWho maintains this page
- 06SourcesWhere this page’s references lead
02The method
What uncertainty quantification and model validation covers.
- 01
Core methods
sensitivity analysis; Monte Carlo propagation; surrogate modelling; Bayesian calibration; model verification; multi-model ensembles.
- 02
Data and instruments
observational benchmarks; model ensembles; expert elicitation; experimental data; parameter databases.
- 03
Validation
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
- 04
Limits
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
- 05
Use in risk decisions
Uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions.
03Reference practice
Uncertainty quantification follows verification and validation practice.
| Reference | What it sets |
|---|---|
| ASME V&V standards | Verification and validation of computational models |
| IPCC uncertainty guidance | Communication of uncertainty |
| Bayesian model calibration | Parameter inference with uncertainty |
04Process
How the process runs.
Process
Uncertainty quantification and model validation: How an economic shock propagates
Input-output models trace a change in final use or primary input prices through industry linkages, with multipliers for output, income and employment.
01 Shock and linkages
- 01ShockDataset · Competent authorityA shock enters as an exogenous change in final uses, or as a change in the price of a primary input.
- 02Input-output linkagesModel · Competent authorityBackward linkages tie an industry to its suppliers, forward linkages to its buyers. The model assumes fixed technical input coefficients.
02 Economy
- 03Sector outputModel · Competent authorityThe quantity model estimates how a change in final uses affects output. Output multipliers give the production needed per unit of final use.
- 04PricesModel · Competent authorityThe price model simulates cost-driven inflation from changes in primary input prices. It assumes perfect competition.
03 People
- 05JobsDataset · Competent authorityEmployment multipliers represent jobs created per unit of currency of additional final use.
- 06Household incomeDataset · Competent authorityIncome multipliers show how final use changes affect household income. A social accounting matrix shows the circular flow of income.
04 State
- 07Public financesStep · Competent authorityEffects on public revenue and spending need government accounts linked to the model, for example in a social accounting matrix.
- 08Policy responseDecision · Competent authorityThe responsible party decides on a response. The same linkages can be used to test it.
Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval. The models assume fixed technical coefficients and are not forecasts.
05In systemic risk work
Uncertainty that survives the chain.
When models are chained, uncertainty compounds. A hazard model’s spread, an exposure dataset’s error and a vulnerability curve’s uncertainty must be propagated together. Without that, the final number looks more certain than any of its parts.
- Propagate uncertainty through chained models.
- Separate aleatory and epistemic uncertainty.
- Use sensitivity analysis to find dominant sources.
- Applications on Systems Modelling and Labs.
06Applied in
Platforms that apply this method.
07Stewardship
Who maintains this page.
Systems Science and Modelling stewards uncertainty quantification and model validation: it keeps this description current and reviews how the method is applied across the platforms.
08Sources
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
09Questions
Questions and answers.
Why does one institution host every method page?
GCRI hosts the STEM Commons so that each method has one canonical description. Platforms link to it rather than repeat it, which keeps definitions consistent.
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