GCRI · Fields across the Consortium
Methods and modelling
Methods and modelling covers the quantitative toolkit for systemic risk: probability and uncertainty, networks and complex systems, scenarios and stress tests, optimisation and decision analysis, and the discipline of validating each.
The field, seen three ways.
Governance and dialogue
No platform yet in this field.
Models fit for coupled, tail-driven problems.
Systemic risk is tail risk with dependence: the losses that matter come from rare combinations. Methods that assume independence or thin tails understate it. The field’s working questions: how to represent dependence (copulas, networks, agent interactions), how to explore deep uncertainty (scenario discovery, robust decision-making), and how to validate models of events that have not happened.
| Problem | Method family | STEM Commons page |
|---|---|---|
| Tail dependence | Extreme value theory, copulas | Extreme value |
| Contagion | Network models | Network science |
| Deep uncertainty | Scenario discovery, robust decision-making | Scenario and stress testing |
| Parameter uncertainty | Bayesian inference, ensembles | Uncertainty quantification |
How the process runs.
Process
Methods and modelling: Build, sign and accept
An admitted mission becomes a cycle project; the build is pinned, verified and signed, and the Global Risks Forum (GRF) records acceptance for exercise, not permission to deploy.
01 Request and sources
- 01Admitted mission becomes a cycle projectRecord · Universe to FoundryAn admitted mission becomes a cycle project with a decision case.
- 02Qualify data sources and resourcesStep · the Global Centre for Risk and Innovation (GCRI)GCRI qualifies data sources and resource offers and builds the scenario models. An offer is not an allocation.
02 Build and verify
- 03Source snapshotRecord · NXS-FAC (blueprints, builds and signing)The snapshot pins the commit, dependencies and generator versions.
- 04Build contractRecord · NXS-FAC (blueprints, builds and signing)The build contract declares the artefact kind, environment and reproducibility envelope.
- 05Integrate and buildModel · GCRIGCRI integrates software and data, signs builds, and tests security and interoperability.
- 06Verification suiteGate · NXS-FAC (blueprints, builds and signing)Required and advisory checks stay separate. Reproducibility needs pinned inputs, declared nondeterminism and independent rebuilds.
03 Sign and admit
- 07Sign the exact artefactStep · NXS-FAC (blueprints, builds and signing)NXS-FAC builds, attests and requests a signature via a scoped signer identity. It holds no key material.
- 08Package admissionStep · NXS-NXQ (software, models and capability lifecycle)NXS-NXQ receives the signed package and keeps admission evidence that binds the task, data class, evaluation suite and expiration.
04 Accept
- 09Core Build ReportRecord · GCRIThe Core Build Report is the go or no-go record. Universe assembles the Core and holds the decision for the exercise.
- 10G3 · Build accepted for exerciseGate · The Global Risks ForumGRF records whether the build is accepted for exercise. A signed build or release candidate is not permission to deploy to production.
The programme year · NXS-FAC · UniverseDesigned programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The record order follows the Builds and Releases room lists of NXS-FAC.
Validation references.
| Framework or body | What it governs or supplies |
|---|---|
| IEC 31010 | Selection of risk assessment techniques |
| Model risk management practice in finance (for example supervisory model risk guidance) | Model inventory, validation and governance |
| EQUATOR reporting guidelines including TRIPOD | Transparent reporting of prediction models |
Methods across platforms.
- Systems Modelling: coupled models.
- Risk Management: enterprise and systemic practice.
- Labs: method experiments.
The methods behind the field.
- Probability and statistics
- Uncertainty quantification and model validation
- Artificial intelligence and machine learning
- Systems engineering
- Causal inference and impact evaluation
- High-performance, cloud and edge computing
- Data engineering, interoperability and stewardship
- Geospatial information systems
- Software and safety-critical systems engineering
- Decision analysis under uncertainty
- Scenario design and stress testing
- Network science and contagion
- Reliability and resilience engineering
- Complex systems and agent-based modelling
- Optimisation and operations research
- Actuarial mathematics
- Extreme value theory and tail risk
- Financial and risk engineering
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Questions and answers.
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