The final entry about reasons to perform risk analysis is really a summary in which we say that the process of risk modelling forces a more structured and rigorous model building and decision evaluation process. Such a process forces consideration of those factors that would lead to a change in an output (and hence require a understanding of the dynamics and relationships in a situation). It also makes explicit those areas where people may be making different assumptions, particularly since static models are much more arbitrary in their choice of variables to include (Event risks? Risk mitigation measures?) as well as in their choice of variables (which, in a static model, may not map with the true drivers of uncertainty in the situation).
As a result, risk models have many advantages, including:
- They place any reference or base static case within the true range of possible outcomes, and hence are less biased.
- They establish the average outcome (the average being a key reference quantity that is not known when building static models in general).
- They allow consideration of output variability in decision-making; such considerations are required in non-financial decision-making, as well as when incorporating risk preferences into financial decision-making.
- They transfer some decision-making (and modelling) responsibility toward the decision-maker (and not just the modeller); it is no longer sufficient or credible to blame the modeller when the true outcome is different to that which the model predict.
- It allows for a wider set of variables (correctly) to be included in the mode.
- Monte Carlo simulation allows modelling aspects, such as correlation and other dependencies, probabilities, and contingent claims can be modelled, whereas this is not generally possible in static modelling.
Dr. Michael Rees
Director of Training and Consulting