The information gathered from the risk workshop process provides a useful starting point for the risk quantification exercise to be carried out by the risk experts.
The risk expert will investigate risk more thoroughly, and designate it a probability, usually within the classifications of high, medium or low. The data required varies, depending on the risk valuation technique adopted.
Simple evaluation technique
risk experts should realistically assess the likelihood of final costs to be above or below the amount included in the raw public sector comparator. The number of point estimates used in valuing the impact of risks (each having a different expected consequence) should reflect the materiality of the risk and the information available. Where empirical evidence is unavailable or incomplete, commonsense approximations may be used.
Impact of risk = consequence x probability of occurrence
The quantification of each risk is then the sum of these probability-weighted consequences (assuming that they are all independent).
EXAMPLE- Consider the risk of plant and equipment price changes during the construction period. The following probabilities and consequences have been estimated: | |||||
| Assumption | Probability | Consequence ($'ooo) | Impact of risk ($'ooo) |
|
| Below base amount | 20% | -5,000 | -1,000 |
|
| No deviation from base amount | 10% | 0 | 0 |
|
| Overrun: likely | 40% | 10,000 | 4,000 |
|
| Overrun: moderate | 20% | 15,000 | 3,000 |
|
| Overrun: extreme | 10% | 20,000 | 2,000 |
|
|
| 100% |
| 8,000 |
|
Note: 'base amount' refers to the cost of the raw plant and equipment estimated in the raw PSC Timing of risk: • 100% during the construction period Allocation of risk: • Transferred to the Private Party | |||||
Advanced evaluation technique
By this stage, the risk experts should be relatively comfortable with the task ahead. However, one potential problem may be reluctance on the part of the risk expert to
provide probability distributions People often consider it more difficult to provide a probability distribution than they do a single point estimate. In fact, there are two components of uncertainty that are included in the distribution: (1) the inherent uncertainty in the variable itself, and (2) the uncertainty arising from the expert's lack of knowledge of the variable. In a risk analysis model these are not differentiated, and it is the combined uncertainty that is required for the model.
Reluctance in estimating a probability distribution can arise from the expert's assumption that his/her lack of knowledge should not be included (whereas in fact there is no alternative - there is no perfect expert). The following points should be highlighted to the risk expert:
(a) providing a distribution for a variable does not require a greater knowledge of the variable than a single point estimate. By contrast, a distribution gives the expert a means to express their lack of exact knowledge
(b) estimation of a probability distribution does not require any great knowledge of probability theory
(c) all that can be expected of them is that they are 90% confident that the risk outcome will lie somewhere within their estimation of the risk
(d) there will be an opportunity to revise the estimates of individual risks at a later stage, particularly if they are found to be significant drivers of the overall risk adjustment.
Considerable reluctance can also be overcome by careful phrasing of a question. For example, if trying to solicit the rates of failure of an average contractor against a service requirement, it makes much more sense for a group of people to be asked, 'Over the last ten year period, how many failures have you had with your present contractor?' and 'How good do you think your contractor is, relative to the average?' rather than 'What is the rate of failure of an average contractor?'