GCRI · بەڕێوەبردنی مەترسیی سیستەمی
ڕێگا بڕی و چۆنایەتییەکان
هەڵسەنگاندنی مەترسی uses numbers where data support them and structured judgement where they do not. Probabilistic models, loss distributions, دەرهێنانی ڕای شارەزا, bow-tie diagrams and risk matrices each answer different questions and fail in different ways. This page sets out how the main methods work, when each is fit for purpose, and how combining them keeps an assessment honest about what is known and what is assumed.
Quantitative and qualitative methods on the record.
Quantitative and qualitative methods: how the risk develops.
Probability, uncertainty and judgement. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Models need data that fit
Frequency and severity models work where events are numerous and recorded consistently. For rare, novel or changing risks the data are thin, and a precise-looking number can carry more assumption than evidence.
Judgement carries biases
Experts anchor on recent events, overestimate their own precision and converge in groups. Structured elicitation reduces these effects by separating estimates, asking for ranges and scoring calibration.
Matrices compress information
Likelihood and consequence matrices are quick to use, but they rank risks coarsely, can reverse orderings and hide correlated losses. They suit screening, and need other methods before a major decision.
Methods combine through structure
Fault trees, event trees and bow-tie diagrams link causes, controls and consequences. They provide a frame into which quantitative estimates and expert judgement can be placed and checked.
Cascade and stress lab.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Cascade and stress lab
Quantitative and qualitative methods · National system · Scenario SC-40
Interdependency graph
red: failed in scenario
Cascade sequence
- T+0HAZHeatwave and drought
- T+6hENGThermal plant derated, cooling water
- T+9hICTData centre on backup power
- T+14hWATPumping reduced, pressure loss
- T+20hHLTHospital water supply at risk
Service loss by system
Methods must be fit for risks that cross domains.
- ئاوهاوبەستیHydrological risk is mostly quantified with simulation and extreme value statistics.What to watchUncertainty ranges reported with return periods
- تەندروستیهاوبەستیEpidemic risk combines transmission models with expert judgement early in outbreaks.What to watchStructured elicitation records
- فرەجۆریی زیندەوەریهاوبەستیEcosystem risks often have qualitative evidence only, which matrices flatten.What to watchQualitative evidence kept as such
Method comparisons are designed for the زنجیرەی Methods and Technical. Cox showed in 2008 that risk matrices can rank risks inconsistently; the page sets out when quantitative, semi-quantitative and qualitative methods each fit.
- Comparisons of assessment techniques applied to the same risk.
- Cases where a ماتریکسی مەترسی misled a decision.
How quantitative and qualitative methods is modelled, measured and tested.
مۆدێلەکە، گریمانەکانی، داتاکانی و نادڵنیاییەکەی، بەو شێوەیەی زانا یان ئەندازیارێک دەیپشکنێت. پێکهاتەکان ڕێگا بڵاوکراوەکان دەگرنەبەر. بەهاکانی کێشراو نموونەییە، نەک دۆزینەوە.
مۆدێل، هاوکێشەکان و گریمانەکان
The model structure used for quantitative and qualitative methods, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)مەترسیدەر، ڕووبەڕووبوون، لاوازی و توانای مامەڵەکردنEAL = ∑k pk · Lkبەسەر ڕووداوەکانی k بە ئەگەری ساڵانەی pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| گریمانە | بۆچی دروست دەکرێت | چۆن تاقی دەکرێتەوە |
|---|---|---|
| مەترسیدەر و ڕووبەڕووبوون لە ناو پێشهاتێکدا سەربەخۆن | ئینتێگراڵی زیان بە ئاسانی دەهێڵێتەوە | بەراورد بکە لەگەڵ گونجاندنی کۆپیولا لەسەر تۆمارە هاوبەشەکان |
| هاوبەستییەکان لە نزیک خاڵی کارکردن هێڵین | ڕێگە دەدات شۆکەکان بڵاو ببنەوە لە A | لە دەرەوەی مەودای هێڵیی شڵەژێنە و بەراورد بکە |
| چەمکەکانی لاوازی لە شوێنێکەوە بۆ شوێنێکی تر دەگوازرێنەوە | چەمکی ناوخۆیی بە دەگمەن بەردەستە | لەگەڵ زیانە چاودێریکراوەکان پشتڕاستی بکەرەوە کە هەن |
- تێکردنەکان
- کۆمەڵە داتای وەشانکراو لەگەڵ ڕەسەنایەتی
- دەرچووەکان
- دابەشبوون، نەک یەک ژمارە
- کۆد
- بڵاوکراوەتەوە لەگەڵ کارتی مۆدێل و تاقیکردنەوەکان
- بەرهەمهێنانەوە
- هەمان داتا، کۆد، ژینگە و تۆو
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | ناونیشان |
|---|---|---|---|---|
| Risks assessed with quantitative estimates | percent of principal risks | annual | Assessment records | REPORTED |
| Calibration of expert estimates | calibration score on seed questions | per elicitation | Structured expert judgement records | چاودێریکراو |
| Loss event records | count of events with cause and amount recorded | continuous | Internal loss databases | چاودێریکراو |
| Assessment uncertainty range | ratio of upper to lower bound | per assessment | Assessment outputs | مەزەندەکراو |
| Controls with tested effectiveness | percent of key controls | annual | Control testing records | REPORTED |
| Method documentation completeness | percent of assessments with method, data and assumptions recorded | annual | Assessment files | REPORTED |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| سەرچاوە | What it gives | Licence class |
|---|---|---|
| تەکنیکەکانی هەڵسەنگاندنی مەترسی لە IEC 31010 | Descriptions, strengths and limits of more than forty techniques | Licensed standard, purchase required |
| EM-DAT international disaster database (CRED, UCLouvain) | Disaster events with human and economic impacts | Free for non-commercial use with registration |
| چوارچێوەی سێندای Monitor and national disaster loss databases | Published reporting of disaster losses against Sendai targets | Public, UN terms of use |
| TU Delft expert judgement database | Past structured expert judgement studies with calibration data | Licence to check |
| Published operational loss data consortia summaries | Aggregate operational loss patterns in financial services | Members only; public summaries (to check) |
Who decides, and what the evidence serves.
مەترسیی سیستەمی Management prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| کێ بڕیار دەدات | The question | Evidence this page serves |
|---|---|---|
| Risk functions and analysts | Which method fits a given risk and decision | Method selection guides; documentation standards |
| Boards and executives | How much confidence to place in an assessment before acting on it | Uncertainty ranges; method documentation; calibration records |
| Regulators and supervisors | Whether an institution’s risk models are fit for the use made of them | Model documentation; validation and back-testing evidence |
Outputs this topic produces
- Method selection guides keyed to the question and the evidence available
- Structured دەرهێنانی ڕای شارەزا protocols with calibration
- Bow-tie and fault-tree templates linked to registers
- Assessment files that record data, assumptions and ranges
What methods experts ask.
- Is there enough relevant data to estimate probabilities, or would numbers convey a false sense of precision?
- What kind of uncertainty dominates: randomness, lack of knowledge, or disagreement about how the system works?
- How sensitive is the decision to the parts of the analysis that are least certain?
- How can expert judgement be elicited so that it is transparent and less prone to bias?
Kinds of uncertainty.
Randomness
Natural variability that persists even with perfect knowledge, such as weather from year to year. Probability handles it well.
Lack of knowledge
Uncertainty that more data or research could reduce. It should be shown, not hidden in a single number.
Disputed structure
Where experts disagree on models, probabilities or values. Scenarios and robust decision methods suit it better.
A toolkit, matched to the problem.
- 01
Statistical and probabilistic models
Frequency analysis, extreme value methods and Bayesian inference from ئەگەر و ئامار. Limits: rare events are poorly sampled, and past data may not reflect a changing system.
- 02
پێوانەکردنی نادڵنیایی
ژماردنی نادڵنیایی و پشتڕاستکردنەوەی مۆدێل propagates input uncertainty through models and tests them against data. Limits: it cannot capture what the model leaves out.
- 03
Structured expert judgement
Formal elicitation protocols that calibrate and combine expert views. Limits: time-consuming, and experts share blind spots.
- 04
Qualitative assessment
Scales, matrices and narrative analysis. Useful for communication and screening, but risk matrices can rank risks inconsistently and should not be the only tool.
How it is studied, and what the methods cannot say.
- 01ئەگەر و ئامارFrequency and severity modelling · Dependence between risks · Combining data and judgementApplied here
- 02ژماردنی نادڵنیایی و پشتڕاستکردنەوەی مۆدێلPropagating input uncertainty · Separating kinds of uncertainty · Deep uncertainty framingApplied here
- 03شیکاری بڕیار لە ژێر نادڵنیاییداMulti-criteria treatment choice · Decision trees for contingent plansApplied here
- 04دیزاینی سیناریۆ و تاقیکردنەوەی گوشارCross-sector scenario design · Reverse stress testing · Second-round effectsApplied here
Where this page’s references lead.
ئەو لەش و بەڵگەنامانەی ئەم لاپەڕەیەیان لەگەڵ دەخوێنرێتەوە. دۆخ و وەشانەکان دەگۆڕێن؛ هەر بەستەرێک دەچێتە لای لەشی دەرکەر.
پرسیار و وەڵام.
Are risk matrices reliable?
What is structured expert judgement?
When should risk be quantified?
Are quantitative methods always better?
What is wrong with risk matrices?
How should uncertainty be communicated?
لەم لاپەڕەیەوە بەڵگە پێشکەش بکە.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- ئیمەیڵدەروازەی GW-GCRIلەم لاپەڕەیەوە بینێرەبەڵگە، ڕاستکردنەوە، بابەت، ڕێگا، پێداچوونەوە، وەرگێڕان یان تێکردنی پڕۆفایل. بە نیشانەی ئەم لاپەڕەیە دەگات، بۆ ئەوەی دەستکاریکەری خاوەنەکەی وەریبگرێت.
- Zenodoتەکیەی کراوەدایبنێ و بیدە بە کۆمەڵگاتۆماری خۆت لە Zenodo بە ORCID iD ی خۆت بڵاو بکەرەوە، پاشان لەوێ بیدە بە کۆمەڵگای Reports. سەرپەرشتیارەکان پێداچوونەوەی بۆ دەکەن؛ DOI هەر هی تۆیە.
- ReportsGCRIهەموو ڕێگاکانی بەشداری ببینەجۆرەکانی بەشداری، هەریەکە چۆن پێداچوونەوەی بۆ دەکرێت، چ ناوبردنێک وەردەگریت، مۆڵەتەکە و ئەوەی هەرگیز وەرناگیرێت.
هەرگیز داتای کەسی، وردەکاریی هەستیاری ژێرخان یان هیچ شتێک مەنێرە کە مافی بڵاوکردنەوەت نییە. بەشدارییەک پێش بەکارهێنان پێداچوونەوەی بۆ دەکرێت؛ ناردنی ماف نابەخشێت بە نووسەرایەتی یان بەڕێوەبردنی ڕاگەیاندن، و پاڵپشتی دارایی هەرگیز دۆزینەوەیەک ناکڕێت.
ڕێگایەک، کۆمەڵە داتایەک یان پرسیارێک بهێنە.
زانیاری و پرسیار ئەمڕۆ کراوەن. پرسیارێک ژمارەی ئاماژەی پێدەدرێت و هەنگاوی دواتر دیاری دەکات؛ داواکاری، پارەدان، تەرخانکردنی کۆمپیوتەر و تۆمارکردن تەنیا کاتێک دەکرێنەوە کە تیم، مەرج و توانای پێداچوونەوەیان هەبێت.
بەڵگەنامە یاساییەکان.
نەخشە، داتا و وێنەکانی ئەم ماڵپەڕە بۆ ناوەڕۆک و زانیاریی گشتی پیشان دراون. ئامۆژگاری، پێشبینی یان ئاگادارکردنەوە نین؛ سنوورەکانی زانیارییەکە لە ڕەتکردنەوەی بەرپرسیارێتی و ئاگاداریی داتادا دەستنیشان کراون.