Propagating input uncertainty
Ranges and distributions for inputs are sampled through risk models so that outputs carry intervals, and the inputs that drive most of the output range are identified.
GCRI · Systemic Risk Management
Uncertainty quantification for risk
Uncertainty quantification states how much a risk estimate could differ from reality and why. In systemic risk management it separates variability from lack of knowledge, propagates uncertainty through models, and shows decision makers which uncertainties matter for their choice.
01Contents
Uncertainty quantification for risk in five parts.
02Application
What uncertainty quantification and model validation does here.
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. In risk management and systemic risk and methods and modelling, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on the STEM Commons.
Separating kinds of uncertainty
Natural variability and incomplete knowledge are kept apart, so that decision makers see which uncertainty could be reduced by further study and which could not.
Deep uncertainty framing
Where probabilities cannot be defended, results are presented across scenarios without weights, and decisions are examined for robustness rather than expected value.
03Monitor
Uncertainty analysis.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Uncertainty analysis
Systemic risk · Uncertainty quantification · Run R-7955
Sensitivity rankedGCRI · Systemic Risk Management
Total-order sensitivity
Output distribution
Inputs
| Risks above appetite | Uniform | risks, stated | expert elicitation |
| Key risk indicators in breach | Normal | KRIs, stated | expert elicitation |
| Controls tested effective | Normal | of controls, stated | expert elicitation |
| Interdependency links mapped | Beta | links, stated | expert elicitation |
04Data
What the method consumes.
Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Model input distributions | Sampling inputs | ESTIMATED |
| Expert elicitation records | Ranges for poorly known parameters | ESTIMATED |
| Observed outcomes | Calibration and validation | OBSERVED |
| Scenario sets | Deep uncertainty exploration | MODELLED |
05Methods and evidence
How uncertainty quantification for risk is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Model, equations and assumptions
The model structure used for uncertainty quantification for risk, the assumptions it makes and how each is tested.
Risk of element i
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityExpected annual loss
EAL = ∑k pk · Lkover events k with annual probability pkCoupled systems
xt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
| Check | How it is done |
|---|---|
| Global sensitivity analysis | Variance-based or other global methods identify which inputs drive output uncertainty |
| Calibration of intervals | Stated intervals are compared with how often outcomes fall inside them |
| Structural uncertainty | Results from alternative model structures are compared and reported together |
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
07Process
How the process runs.
Process
Uncertainty quantification for risk: Hazard, exposure, vulnerability
In IPCC AR6 Working Group II, climate risk results from interactions between hazards and a system’s exposure and vulnerability, and responses can add risk.
01 Drivers
- 01HazardDataset · Chief risk officerThe potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.
- 02ExposureDataset · Chief risk officerThe presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.
- 03SensitivityDataset · Chief risk officerThe degree to which a system or species is affected, either adversely or beneficially, by climate variability or change.
- 04Capacity to cope and adaptDataset · Chief risk officerThe ability of systems, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.
02 Assessment
- 05Risk from responsesComponent · Chief risk officerRisks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.
- 06VulnerabilityComponent · Chief risk officerThe propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.
- 07RiskModel · Systemic Risk ManagementRisk results from dynamic interactions between hazards and the system’s exposure and vulnerability. Each may be uncertain and change.
03 Response
- 08Risk management and responseStep · Chief risk officerRisk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.
- 09Residual riskRecord · Chief risk officerThe climate impact risk that remains after adaptation and mitigation efforts. Adaptation can redistribute risk between areas and groups.
04 Decision
- 10Decision by the responsible partyDecision · Chief risk officerThe framing names no single decision maker. Decisions on responses rest with the responsible party under its own rules.
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.
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Systems Science and Modelling stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of uncertainty quantification and model validation in this field?
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
How are results checked?
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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