Disaggregation

See also: Modeling expert opinion introduction

A key technique to eliciting distributions of opinion is to disaggregate the problem sufficiently well so that the expert can concentrate on estimating something that is tangible and easy to envisage. For example, it will generally be more useful to ask the expert to break down his/her company's revenue into logical components (like region, product, subsidiary company, etc.) rather than to estimate the total revenue in one go. Disaggregation allows the expert and analyst to recognise dependencies between components of the total revenue. It also means that the risk analysis result will be less critically dependent on the estimate of each model component. Aggregating the estimates of the various revenue components will show a more complex and accurate distribution than ever could have been achieved by directly estimating the sum. The aggregation will also take care of the effects of Central Limit Theorem automatically - something that is extremely hard for the expert to do in his/her head. Another benefit of disaggregation is that the logic of the problem usually becomes more apparent and the model therefore becomes more realistic.

During the disaggregation process, the analyst should be aware of where the key uncertainties lie within the model and therefore where to place the most emphasis. The analyst can check whether an appropriate level of disaggregation has been achieved by running a sensitivity analysis on the model and looking to see whether the model's output uncertainty is dominated by one or two model inputs.

Read on: Maximum entropy

 

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