The quality of a risk analysis
See also: Risk management introduction
Risk analyses are often of poor quality. They might also be very expensive, take a long time to complete and use up valuable human resources. In fact, the more complex and expensive a quantitative risk analysis is, the more likely it is to be of poor quality. Worst of all, the people making decisions on the results of these analyses have little if any idea of how bad they are. These are rather attentiongrabbing sentences but this topic is small and you really should not to skip over it: it could save you a lot of heartache.
The reasons a risk analysis can be terrible
To give some motivation for this topic, let us look at some of the results of a survey we ran some years ago in a welldeveloped sciencebased area of risk analysis. The question appears in the title of each pane. Which results do you find most worrying?
From the first of the figures above you'll see that there really needs to be more communication between decisionmakers and their risk analysts and a greater attempt to work as a team. The risk analyst can be an important liaison between those 'on the ground' who understand the problem at hand and hold the data and those who make decisions. The risk analyst needs to understand the context of the decision question and have the flexibility to be able to find the method of analysis that gives the most useful information. Too often risk analysts complain that they get told to produce a quantitative model by the boss, but have to make the numbers up because the data aren't there. Now doesn't that seem silly? Surely the decisionmaker would be none too happy to know the numbers are all made up, but the risk analyst is often not given access to the decisionmakers to let them know. On the other hand, in some business and regulatory environments they are trying to follow a rule that says a quantitative risk analysis needs to be completed  the box needs ticking.
Regulations and guidelines can be a real impediment to creative thinking. Our founder, David Vose, has been in plenty of committees gathered to write risk analysis guidelines and he does his best to reverse the tendency to be formulaic. His argument is that in well over twenty years he have never done the same risk analysis twice: every one has its individual peculiarities. Risk analysis should not be a packaged commodity but a voyage of reasoned thinking leading to the best possible decision at the time.
It is usually pretty easy to see early on in the risk analysis process that a quantitative risk analysis will be of little value. There are several key areas where it can fall down:

It can't answer all the key questions;

There are going to be a lot of assumptions;

There is going to be one or more showstopping assumption; and

There aren't enough good data or experts.
One can get around (1) sometimes by doing different risk analyses for different questions, but that can be problematic when each risk analysis has a different set of fundamental assumptions  how do we compare their results?
For (2) you need to have some way of expressing whether a lot of little assumptions compound to make a very vulnerable analysis: if you have 20 assumptions (and 20 is quite a small number), all pretty good ones  e.g. we think there's a 90% chance they are correct, but the analysis is only useful if all the assumptions are correct, then we only have a 0.9^{20} = 12% chance that the assumption set is correct. Of course, if this was the real problem we wouldn't bother writing models. In reality, in the business world particularly, we deal with assumptions that are good enough because the answers we get are close enough. In some more scientific areas, like human health, we have to deal with assumptions like: compound X is present; compound X is toxic; people are exposed to compound X; the exposure is sufficient to cause harm; and treatment is ineffective. The sequence then produces the theoretical human harm we might want to protect against, but if any one of those assumptions is wrong there is no human health threat to worry about.
If (3) occurs we have a pretty good indication that we don't know enough to produce a decent risk analysis model, but maybe we can produce two or three crude models under different possible assumptions and see whether we come to the same conclusion anyway.
(4) is the least predictable area because the risk analyst doing a preliminary scoping can be reassured that the relevant data are available, but then finds out they are not available either because the data turn out to be clearly wrong (one sees this a lot, even in peerreviewed work), the data aren't what was thought, there is a delay past the deadline in the data becoming available, or the data are dirty and need so much rework that it becomes impractical to analyze them within the decision timeframe.
There is a lot of emphasis placed on transparency in a risk analysis, which usually manifests itself in a large report describing the model, all the data and sources, the assumptions, etc. and then finishes with some graphical and numerical risk analysis outputs. I've seen reports of one or two hundred pages and that seem far from transparent to me  who really has the time or inclination to read such a document? The executive summary tends to focus on the decision question and numerical results, and places little emphasis on the robustness of the study.
Communicating quality of data
Elsewhere in this help file you will find lots of techniques for describing the numerical accuracy that a model can provide given the data that are available. These analyses are at the heart of a quantitative risk analysis and give us distributions, percentiles, sensitivity plots, etc.
We will now look at how to communicate any impact on the robustness of a model due to the assumptions behind using data or settling on a model scope and structure. We encourage the risk analyst to write down each assumption that is made in developing equations and performing statistical analyses.
We get participants to do the same in the training courses we teach as they solve simple class exercises and there is a general surprise at how many assumptions are implicit in even the simplest type of equations. It becomes rather onerous to write all these assumptions down, but it is even more difficult to convert the conceptual assumptions underpinning our probability models into something that a reader rather less familiar with probability modelling might understand.
The NUSAP method (Numeral Unit Spread Assessment Pedigree, Funtowicz and Ravetz, 1990) is a notational system that communicates the level of uncertainty for data in scientific analysis used for policy making. The idea is to use a number of experts in the field to score independently the data under different categories. The system is wellestablished as being useful in toxicological risk assessment. We describe here a generalization of the idea. It's key attractions are that it is easy to implement and can be summarised into consistent pictorial representations.
In the table below we used the categorisation descriptions of data from van der Sluijs et al (2005), which are: proxy  reflecting how close data being used are to ideal; empirical  reflecting the quantity and quality of the data; method  reflecting where the method used to collect the data lay between careful and wellestablished, and haphazard; and validation  reflecting whether the acquired data have been matched to real world experience (e.g. does an effect observed in a laboratory actually occur in the wider world).
Pedigree matrix for parameter strength (adapted from Boone et al, 2007).
Each data set is scored in turn by each expert. The average of all scores is calculated and then divided by the maximum attainable score of 4. For example:

Proxy 
Empirical 
Method 
Validation 
Expert A 
3 
2 
4 
3 
Expert B 
3 
2 
4 
3 
Expert C 
2 
1 
3 
4 
gives an average score of 2.833. Dividing by the maximum score of 4 gives 0.708. An additional level of sophistication is to allow the experts to weight their level of expertise for the particular variable in question (e.g. 0.3 for low, 0.6 for medium and 1.0 for high, as well as allowing experts to select not to make any comment when it is outside their competence) in which case one calculates a weighted average score. One can then plot these scores together and segregate them by different parts of the analysis if desired, which gives an overview of the robustness of data used in the analysis:
Plot of average scores for data sets in a toxicological risk assessment
Scores can be generally categorized as follows:
<0.2 weak
0.2  0.4 moderate
>0.4  0.8 high
>0.8 excellent
We can summarize the scores for each data set using a kite diagram to give a visual 'traffic light' green representing the parameter support is excellent, red representing weak, and one or two levels of orange representing gradations between these extremes. The figure below gives an example: one works from the centre point marking on the axes the weighted fraction of all the experts considering the parameter support to be 'excellent', then add the weighted fraction considering the support to be 'high' etc. These points are then joined to make the different color zones  from green in the centre for 'excellent', through yellow and orange, to red in the last category: a kite will be green if all experts agree the parameter support is excellent and red for weak.
Plotting these kite diagrams together can give a strong visual representation: a sea of green should give great confidence, a sea of red says the risk analysis is extremely weak. In practice, we'll end up with a big mix of colors but over time one can get a sense of what color mix is typical, when an analysis is comparatively weak or strong, and when it can be relied upon for your field.
Summarizing the level of data support the experts believe that a model
parameter will have: red (dark) in the outer ban = weak; green (light)
in the inner band = excellent.
The only real impediment to using the system above is that you need to develop a database software tool. We have developed the Pelican risk management system for this purpose.
Level of criticality
The categorisation system described above helps determine whether a parameter is wellsupported, but it can still misrepresent the robustness of the risk analysis. For example, we might have done a food safety microbial risk analysis involving ten parameters  nine enjoy high or excellent support, and one is suffering weak support. If that weakly supported parameter is defining the doseresponse relationship (the probability a random individual will experience an adverse health effect given the number of pathogenic organisms ingested) then the whole risk analysis is jeopardized because the doseresponse is the link between all the exposure pathways and the amount of pathogen involved (often a big model), and the size of human health impact that results. It is therefore rather useful to separate the kite diagrams and other analyses into different categories for the level of dependence the analysis has on each parameter: critical, important and small, for example.
A more sophisticated version for separating the level of dependence is to statistically analyze the degree of effect each parameter has on the numerical result: for example, one might look at the difference in the mean of the model output when the parameter distribution is replaced by its 95^{th} and 5^{th} percentile. Take that range and multiply by (1the support score, giving 0 for excellent, 1 for terrible), gives one a sense of the level of vulnerability of the output numbers. However, this method suffers other problems. Imagine that we are performing a risk analysis on an emerging bacterium for which we have absolutely no doseresponse data so we use a data set for a surrogate bacterium that we think will have a very similar effect (e.g. because it produces a similar toxin). We might have large amounts of excellent data for the surrogate bacterium and may therefore have little uncertainty about the doseresponse model so using the 5^{th} and 95^{th} percentiles of the uncertainty about that doseresponse model will result in a small change in the output and multiplying that by (1the support score) will underrepresent the real uncertainty. A second problem is that we often estimate two or more model parameters from the same data set: for example, a doseresponse model often has two or three parameters that are fitted to data. Each parameter might be quite uncertain but the doseresponse curve can be nonetheless quite stable, so this numerical analysis needs to look at the combined effect of the uncertain parameters as a single entity which requires a fair bit of number juggling.
The biggest uncertainty in a risk analysis
The techniques discussed above have focused on the vulnerability of the results of a risk analysis to the parameters of a model. When we are asked to review or audit a risk analysis the client is often surprised that our first step is not to look at the model mathematics and supporting statistical analyses, but to consider what the decision questions are, whether there were a number of assumptions, whether it would be possible to do the analysis a different (usually simpler, but sometimes more complex and precise) way, whether this other way would give the same answers and see if there are any means for comparing predictions against reality. What we are trying to do is see whether the structure and scope of the analysis is correct. The biggest uncertainty in a risk analysis is whether we started off analyzing the right thing and in the right way.
Finding the answer is very often not amenable to any numerical technique because we will not have any alternative to compare against. If we do, it might nonetheless take a great deal of effort to put together an alternative risk analysis model and a model audit is usually too late in the process to be able to start again. A much better idea, in my view, is to get a sense at the beginning of a risk analysis, on how confident we should be that the analysis will be scoped sufficiently broadly, or how confident we are that the world is adequately represented by our model. Needless to say, we can also start rather confident that our approach will be quite adequate and then, once having delved in to the details of the problem, find out we were quite mistaken so it is important to keep revisiting our view of the appropriateness of the model.
We encourage clients, particularly in the scientific areas of risk that we work in, to instigate a solid brainstorming session of experts and decisionmakers whenever it has been decided that a risk analysis is to be undertaken, or maybe is just under consideration.
The focus is to discuss the form and scope of the potential risk analysis.
The experts first of all need to think about the decisionquestions, discuss with decisionmakers any possible alternatives or supplements to those questions and then consider how they can be answered, and what the outputs should look like (e.g. only the mean is required, or some high percentile). Each approach will have a set of assumptions which need to be thought through carefully: what would the effect be if the assumptions are wrong? If we use a conservative assumption and estimate a risk that is too high, are we back to where we started? We need to think about data requirements too: are the quality likely to be good and the data easily attainable?
When the brainstorming is over, we recommend that you pass around a questionnaire to each expert and ask those attending to independently answer a questionnaire something like this:
We discussed three risk analysis approaches (description A, description B, description C). Please indicate your level of confidence (0 = none, 1 = slight, 2 = good, 3 = excellent, 1 = no opinion) to the following:

What is your confidence that Method (A,...B,C) will be sufficiently flexible and comprehensive to answer any foreseeable questions from the management about this risk?

What is your confidence that Method (A,...B,C) is based on assumptions that are correct?

What is your confidence for Method (A,...B,C) that the necessary data will be available within the required timeframe and budget?

What is your confidence that the Method (A,...B,C) analysis will be completed in time?

What is your confidence that there will be strong support for Method (A,...B,C) among reviewing peers?

What is your confidence that there will be strong support for Method (A,...B,C) among stakeholders?
Asking each brainstorming participant independently will help you attain a balanced view, particularly if the chairperson of that meeting has enforced the discipline of requiring participants not to express their view on the above questions during the meeting (it won't be completely possible, but you are trying to make sure that nobody will be influenced into giving a desired answer). Asking people independently rather than trying to achieve consensus during the meeting will also help remove the overconfidence that often appears when people make a group decision.
Iterate
Things change. The political landscape in which the decision is to be made can become more hostile or accepting to some assumptions, data can prove better or worse than we initially thought, new data turn up, new questions suddenly become important, the time frame or budget can change, etc.
So it makes sense to go back from time to time over the types of assumptions analysis discussed above and to remain open to taking a different approach, even to making as dramatic a change as going from a quantitative to a qualitative risk analysis. That means you (analysts and decision makers alike) should also be a little guarded in making premature promises so you have some space to adapt. In our consultancy contracts, for example, a client will usually commission us to do a quantitative risk analysis and tell us about the data they have. We'll probably have had a little look at the data too. We prefer to structure our proposal into stages.
In the first stage we go over the decision problem, review any constraints and (time, money, political, etc.), take a first decent look at the available data, and figure out possible ways of getting to the answer. Then we produce a report describing how we want to tackle the problem and why. At that stage the client can stop the work, continue with us, do it themselves, or maybe hire someone else if they wish. It may take a little longer (usually a day or two) but everyone's expectations are kept realistic, we are not cornered into doing a risk analysis that we know is inappropriate, and clients don't waste their time or money. As consultants we are in the somewhat privileged position of turning down work that we know would be terrible. A risk analyst employed by a company or government department may not have that luxury.
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 VoseDepletion
 VoseDepletionFlag
 VoseDepletionTime
 VosejProduct
 VoseCholesky
 VoseTimeSimulate
 VoseNBootSeries
 VosejkProduct
 VoseRuinSeverity
 VoseRuin
 VosejkSum
 VoseTimeDividendsA
 VoseRuinNPV
 VoseTruncData
 VoseSample
 VoseIdentity
 VoseCopulaSimulate
 VoseSortA
 VoseFrequencyCumulA
 VoseAggregateDeduct
 VoseMeanExcessP
 VoseProb10
 VoseSpearmanU
 VoseSortD
 VoseFrequencyCumulD
 VoseRuinMaxSeverity
 VoseMeanExcessX
 VoseRawMoment3
 VosejSum
 VoseRawMoment4
 VoseNBootMoments
 VoseVariance
 VoseTimeShortTermInterestRateA
 VoseTimeLongTermInterestRateA
 VoseProb
 VoseDescription
 VoseCofV
 VoseAggregateProduct
 VoseEigenVectors
 VoseTimeWageInflationA
 VoseRawMoment1
 VosejSumInf
 VoseRawMoment2
 VoseShuffle
 VoseRollingStats
 VoseSplice
 VoseTSEmpiricalFit
 VoseTimeShareYieldsA
 VoseParameters
 VoseAggregateTranche
 VoseCovToCorr
 VoseCorrToCov
 VoseLLH
 VoseTimeSMEThreePoint
 VoseDataObject
 VoseCopulaDataSeries
 VoseDataRow
 VoseDataMin
 VoseDataMax
 VoseTimeSME2Perc
 VoseTimeSMEUniform
 VoseTimeSMESaturation
 VoseOutput
 VoseInput
 VoseTimeSMEPoisson
 VoseTimeBMAObject
 VoseBMAObject
 VoseBMAProb10
 VoseBMAProb
 VoseCopulaBMA
 VoseCopulaBMAObject
 VoseTimeEmpiricalFit
 VoseTimeBMA
 VoseBMA
 VoseSimKurtosis
 VoseOptConstraintMin
 VoseSimProbability
 VoseCurrentSample
 VoseCurrentSim
 VoseLibAssumption
 VoseLibReference
 VoseSimMoments
 VoseOptConstraintMax
 VoseSimMean
 VoseOptDecisionContinuous
 VoseOptRequirementEquals
 VoseOptRequirementMax
 VoseOptRequirementMin
 VoseOptTargetMinimize
 VoseOptConstraintEquals
 VoseSimVariance
 VoseSimSkewness
 VoseSimTable
 VoseSimCofV
 VoseSimPercentile
 VoseSimStDev
 VoseOptTargetValue
 VoseOptTargetMaximize
 VoseOptDecisionDiscrete
 VoseSimMSE
 VoseMin
 VoseMin
 VoseOptDecisionList
 VoseOptDecisionBoolean
 VoseOptRequirementBetween
 VoseOptConstraintBetween
 VoseSimMax
 VoseSimSemiVariance
 VoseSimSemiStdev
 VoseSimMeanDeviation
 VoseSimMin
 VoseSimCVARp
 VoseSimCVARx
 VoseSimCorrelation
 VoseSimCorrelationMatrix
 VoseOptConstraintString
 VoseOptCVARx
 VoseOptCVARp
 VoseOptPercentile
 VoseSimValue
 VoseSimStop
 Precision Control Functions
 VoseAggregateDiscrete
 VoseTimeMultiGARCH
 VoseTimeGBMVR
 VoseTimeGBMAJ
 VoseTimeGBMAJVR
 VoseSID
 Generalized Pareto Distribution (GPD)
 Generalized Pareto Distribution (GPD) Equations
 ThreePoint Estimate Distribution
 ThreePoint Estimate Distribution Equations
 VoseCalibrate
 ModelRisk interfaces
 Integrate
 Data Viewer
 Stochastic Dominance
 Library
 Correlation Matrix
 Portfolio Optimization Model
 Common elements of ModelRisk interfaces
 Risk Event
 Extreme Values
 Select Distribution
 Combined Distribution
 Aggregate Panjer
 Interpolate
 View Function
 Find Function
 Deduct
 Ogive
 AtRISK model converter
 Aggregate Multi FFT
 Stop Sum
 Crystal Ball model converter
 Aggregate Monte Carlo
 Splicing Distributions
 Subject Matter Expert (SME) Time Series Forecasts
 Aggregate Multivariate Monte Carlo
 Ordinary Differential Equation tool
 Aggregate FFT
 More on Conversion
 Multivariate Copula
 Bivariate Copula
 Univariate Time Series
 Modeling expert opinion in ModelRisk
 Multivariate Time Series
 Sum Product
 Aggregate DePril
 Aggregate Discrete
 Expert
 ModelRisk introduction
 Building and running a simple example model
 Distributions in ModelRisk
 List of all ModelRisk functions
 Custom applications and macros
 ModelRisk functions explained
 Tamara  project risk analysis
 Introduction to Tamara project risk analysis software
 Launching Tamara
 Importing a schedule
 Assigning uncertainty to the amount of work in the project
 Assigning uncertainty to productivity levels in the project
 Adding risk events to the project schedule
 Adding cost uncertainty to the project schedule
 Saving the Tamara model
 Running a Monte Carlo simulation in Tamara
 Reviewing the simulation results in Tamara
 Using Tamara results for cost and financial risk analysis
 Creating, updating and distributing a Tamara report
 Tips for creating a schedule model suitable for Monte Carlo simulation
 Random number generator and sampling algorithms used in Tamara
 Probability distributions used in Tamara
 Correlation with project schedule risk analysis
 Pelican  enterprise risk management
 ModelRisk Cloud system
 ModelRisk Cloud introduction
 Getting your software ready
 Starting ModelRisk Cloud
 Uploading a risk analysis model
 Creating a new scenario for the risk analysis model
 Running a Monte Carlo simulation of the model
 Uploading a SID (Simulation Imported Data file)
 Building a risk analysis model that uses SIDs
 Viewing the Monte Carlo results from a simulation run
 Administrator's use of ModelRisk Cloud
 Preparing a risk analysis model for upload to ModelRisk Cloud
 ModelRisk Result Viewer
Enterprise Risk Management software (ERM)
Learn more about our enterprise risk analysis management software tool, Pelican