Cross-sector scenario design
Scenarios are built with a trigger, severity, timing and conditions, then traced through the dependencies of energy, water, communications, finance and public services, with every assumption written down.
GCRI · Systemic Risk Management
Scenario design and stress testing
Scenario design and stress testing are the method this platform stewards for the network. They turn severe but plausible events into structured tests of plans, buffers and dependencies. In systemic risk management they are used to probe cross-sector cascades, to find breaking points and to compare preparedness across institutions on common assumptions.
01Register
Scenario design and stress testing on the record.
Method recordSTEM method
- MethodScenario design and stress testing
- StewardSystemic Risk Management
- Applied inSystemic Risk Management
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What scenario design and stress testing does here.
Scenario design and stress testing construct plausible, severe future conditions to examine how systems and institutions would perform. They complement probabilistic analysis where data are scarce or futures are deeply uncertain. In risk management and systemic risk and methods and modelling, stress tests reveal vulnerabilities that arise when several risks materialise together. They inform capital, contingency and coordination plans across sectors. The canonical description is on the STEM Commons.
Reverse stress testing
Starting from a defined failure, such as a service stopping, analysts search for combinations of events that would cause it and document which ones are plausible.
Second-round effects
Tests add the reactions of institutions during a crisis, such as asset sales, supply hoarding or service cuts, to see how responses amplify or dampen the initial shock.
03Simulator
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Systemic Risk Management in Foundry
- Tested at Systemic Risk Management in Universe
- Codified in Systemic Risks in Standards
- Published in Systemic Risk Management in Reports
- Taught in Faculty of Risk Science and Methods in Academy
04Data
What the method consumes.
Datasets and records used by scenario design and stress testing on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| NGFS climate scenarios | Reference physical and transition pathways | MODELLED |
| Reasonable worst-case planning scenarios | Civil contingency assumptions | Published statistics |
| Historical event reconstructions | Calibration of severity | OBSERVED |
| Dependency inventories | Propagation structure | DECLARED |
05Methods and evidence
How scenario design and stress testing 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 scenario design and stress testing, 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.
Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.
| Check | How it is done |
|---|---|
| Plausibility review | Scenarios are reviewed by domain experts for internal consistency and plausibility before use |
| Severity benchmarking | Scenario severity is compared with historical events and published reference scenarios |
| Model coverage | The share of mapped critical dependencies represented in the test is reported with the results |
Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.
07Connections
Topics that rely on this method.
08Stewardship
Who maintains the method.
Systemic Risk Management stewards scenario design and stress testing for the Consortium and reviews how it is applied across platforms.
09Questions
Questions and answers.
What are the limits of scenario design and stress testing in this field?
Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.
How are results checked?
Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.
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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