GCRI · Agency

Decision analysis for technical assistance

Decision analysis structures the choices technical assistance raises: which expert to engage, which engagement type fits, whether a declared conflict can be mitigated, whether to deploy to a field site, and whether to continue, rescope or close. It makes criteria, weights, uncertainty and value judgements explicit so that the holder of the decision can examine them.

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01Register

Decision analysis for technical assistance on the record.

Method recordSTEM method
  1. MethodDecision analysis under uncertainty
  3. Applied inAgency
  6. Canonical pageSTEM Commons
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 governance and institutions and development finance, 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.

Multi-criteria assessment of expert fit

Competence, context experience, language, availability and independence are scored against criteria set before profiles are seen. Weights are disclosed, evidence labels are kept beside each score, and sensitivity to the weights is reported.

Value of information before deployment

Before a field assignment, the decision holder asks whether a further security, health or access assessment would change the decision. Assessments that could change it are commissioned; those that could not are not.

Adaptive pathways for continuation

Programmes set signposts and triggers in advance, such as counterpart turnover or a failed diagnostic, that prompt a review to continue, rescope or close. Options that keep later choices open are identified early.

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

Agency · Decision analysis for technical · Run R-5933

Sensitivity rankedGCRI · Agency

Total-order sensitivity

Output distribution

Inputs

Requests receivedBetarequests, statedexpert elicitation
Teams formedUniformteams, statedexpert elicitation
Experts on rosterNormalexperts, statedexpert elicitation
Conflicts declaredBetadeclarations, statedexpert elicitation
Uncertainty analysis: the screen’s structure, terms and states for Agency.Example names and illustrative values; a design, not a record or a live system.
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.

DataRoleLabel
Competence evidence with labelsScoring inputsREPORTED
Structured expert elicitationProbabilities where records are absentESTIMATED
Risk registers and incident recordsLikelihood and consequence inputsREPORTED
Cost estimates and budgetsConsequence of each optionESTIMATED
Evaluation findingsRetrospective evidence on similar choicesPublished statistics
05Methods and evidence

How decision analysis for technical assistance 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.

Datasets and provenance

The data classes behind decision analysis for technical assistance and what is recorded about each before it is used.

Data classProvenance recordedResolution statedLicence checked
Satellite imagery, optical and radarsensor, processing level, acquisition datepixel size and revisitper scene
Reanalysis and climate model outputmodel, experiment, member, versiongrid and time stepper release
In-situ sensors and gaugesstation, instrument, calibration recordpoint and sampling rateper network
Population and asset exposuremethod, vintage, input sourcescell size and unitper release
Boundaries and reference geographyproducer, edition, datescaleper edition
Lineage
W3C PROV record from source to figure
Principles
FAIR: findable, accessible, interoperable, reusable
Gaps
shown as missing, never filled silently
Data classes and the provenance fields recorded for each; a dataset is listed by name only once its licence admits the use.
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.

Analyses are checked through sensitivity analysis, peer review and stakeholder scrutiny. Retrospective review compares decisions with outcomes.

CheckHow it is done
Weight sensitivityRankings are recomputed across plausible weight sets; choices that flip are flagged for the decision holder
Evidence and preference kept apartScores record what the evidence shows; weights record what the decision holder values, and each is documented separately
Panel independenceEveryone scoring or eliciting declares interests, and conflicted members recuse from the affected choices
Retrospective reviewPast decisions are compared with outcomes to test whether criteria and probabilities were sound

Results depend on elicited probabilities and preferences that may be biased. Deep uncertainty can make expected value comparisons misleading.

07Process

How the process runs.

Process

Decision analysis for technical assistance: 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.

  1. 01 Drivers

    1. 01HazardDataset · Agency coordinatorThe potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.
    2. 02ExposureDataset · Agency coordinatorThe presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.
    3. 03SensitivityDataset · Agency coordinatorThe degree to which a system or species is affected, either adversely or beneficially, by climate variability or change.
    4. 04Capacity to cope and adaptDataset · Agency coordinatorThe ability of systems, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.

  2. 02 Assessment

    1. 05Risk from responsesComponent · Agency coordinatorRisks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.
    2. 06VulnerabilityComponent · Agency coordinatorThe propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.
    3. 07RiskModel · AgencyRisk results from dynamic interactions between hazards and the system’s exposure and vulnerability. Each may be uncertain and change.

  3. 03 Response

    1. 08Risk management and responseStep · Agency coordinatorRisk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.
    2. 09Residual riskRecord · Agency coordinatorThe climate impact risk that remains after adaptation and mitigation efforts. Adaptation can redistribute risk between areas and groups.

  4. 04 Decision

    1. 10Decision by the responsible partyDecision · Agency coordinatorThe 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.

Systemic Risk Management stewards decision analysis under uncertainty for the Consortium and reviews how it is applied across platforms.

Canonical page: Decision analysis under uncertainty

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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