Choosing an instrument mix
Instrument combinations are compared on cost, speed and coverage across simulated disaster years, with the decision maker's priorities set explicitly.
GRA · Disaster Risk Finance
Spatial financeWhere capital meets hazard, exposure and transition
Decision analysis under uncertainty for disaster risk finance
Decision analysis structures choices under uncertainty. In disaster risk finance it helps governments choose combinations of instruments, set triggers and weigh speed, cost and reliability, making their value judgements explicit.
01Contents
Decision analysis under uncertainty for disaster risk finance in five parts.
02Application
What decision analysis under uncertainty does here.
Decision analysis under uncertainty provides structured methods for choosing among options when outcomes are uncertain and objectives conflict. It makes trade-offs and value judgements explicit. In disaster and crisis, sovereign and public finance, development finance and insurance and catastrophe, decision analysis helps choose actions that perform acceptably across interconnected risks and uncertain futures. It clarifies when to act now and when to keep options open. The canonical description is on the STEM Commons.
Trigger threshold choice
The costs of false alarms and missed events are weighed to choose a trigger threshold for anticipatory finance.
Release rules for contingency funds
Decision rules for releasing funds are designed and tested against past events to check timeliness and fairness.
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
Disaster risk finance · Decision analysis · Run R-1927
Sensitivity rankedGRA · Disaster Risk Finance
Total-order sensitivity
Output distribution
Inputs
| Contingent liability, 1-in-50 | Beta | of GDP, stated | expert elicitation |
| Pre-arranged finance | Lognormal | of 1-in-50 need, stated | expert elicitation |
| Time to disburse | Uniform | days, stated | expert elicitation |
| Basis risk | Uniform | of events, stated | expert elicitation |
04Data
What the method consumes.
Datasets and records used by decision analysis under uncertainty on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Simulated loss years | Scenario inputs | MODELLED |
| Instrument cost data | Options | REPORTED |
| Past event records | Rule testing | OBSERVED |
05Loss model
Loss exceedance, average annual loss and probable maximum loss.
The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks every catastrophe model uses. Teaching model with stated illustrative values, not a prediction.
Controls
- Typical annual hazard intensity (location)
- 1.00 intensity index
- Spread of extremes (scale)
- 0.80 intensity index
- Intensity at which half the value is lost
- 4.00 intensity index
- Dispersion of vulnerability
- 0.55
- Share of value in the hazard footprint
- 40%
- Protection standard (return period)
- 50 years
Readouts
- Average annual loss
- 0.51% of value
- Loss at 1 in 100 years
- 24.5% of value
- Loss at 1 in 250 years
- 28.4% of value
- Intensity at 1 in 100 years
- 4.68 intensity index
Numbers, methods and sources
| Return period (years) | Intensity (intensity index) | Loss (% of value) |
|---|---|---|
| 1 | 0.09 | 0.00 |
| 2 | 1.27 | 0.00 |
| 4 | 1.91 | 0.00 |
| 7 | 2.47 | 0.00 |
| 13 | 3.00 | 0.00 |
| 24 | 3.51 | 0.00 |
| 44 | 4.02 | 0.00 |
| 82 | 4.53 | 23.55 |
| 154 | 5.03 | 26.44 |
| 287 | 5.53 | 28.87 |
| 536 | 6.03 | 30.88 |
| 1,000 | 6.53 | 32.53 |
Methods
- Gumbel extreme-value hazard Gumbel (1958), Statistics of Extremes, Columbia University Press
- Lognormal fragility (vulnerability) FEMA (2003), HAZUS-MH Technical Manual
- Exceedance-probability curve and average annual loss Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
- Levee effect Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- 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
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Rivers Natural Earth
- Urban extents Natural Earth
- Cities Natural Earth
- Earthquakes M4.5+, past week (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Built-up and settlement extent, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Disaster Risk Finance in Foundry
- Tested at Disaster Risk Finance in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Risk Finance in Reports
- Taught in Faculty of Hazards, Disasters and Resilience in Academy
06Validation
How results are checked.
Analyses are checked through sensitivity analysis, peer review and stakeholder scrutiny. Retrospective review compares decisions with outcomes.
| Check | How it is done |
|---|---|
| Values explicit | Weights and priorities are stated by the decision maker |
| Robustness | Choices are tested across scenarios and models |
| Hindsight separated | Past decisions are judged on information available at the time |
Results depend on elicited probabilities and preferences that may be biased. Deep uncertainty can make expected value comparisons misleading.
07Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Financing instruments by layer
How retention and transfer instruments could share losses from decision analysis under uncertainty for disaster risk finance, from frequent to extreme.
| Layer | Who holds the risk | Speed of funds | Main limit |
|---|---|---|---|
| Retention and reserves | Budget, contingency funds | Immediate | Size of the budget |
| Contingent credit | Development banks, ministries of finance | Days to weeks | Debt capacity |
| Parametric cover and risk pools | Regional pools, insurers | Days, on a trigger | Basis risk |
| Reinsurance and catastrophe bonds | Reinsurers, capital markets investors | Weeks to months | Cost of capital |
| Residual loss | Households, firms, humanitarian funding | Uncertain | Unfunded |
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
09Stewardship
Who maintains the method.
Systemic Risk Management stewards decision analysis under uncertainty 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 decision analysis under uncertainty in this field?
Results depend on elicited probabilities and preferences that may be biased. Deep uncertainty can make expected value comparisons misleading.
How are results checked?
Analyses are checked through sensitivity analysis, peer review and stakeholder scrutiny. Retrospective review compares decisions with outcomes.
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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