ABSTRACT
Quantitative risk analysis is one of the most useful tools for avoiding the biases and blind spots of subjective methods. Good models help us to consider plausible but more extreme possibilities that may be ignored due to availability bias or over-optimism. One of the major advantages of quantitative models is that the underlying assumptions are obvious and therefore open to scrutiny. Quantitative analysis can be used to visualise risk, helping to build risk awareness. It can help us to account for interactions between multiple risks to see the net effects; something that would be difficult to achieve with subjective approaches alone. Quantitative risk models allow us to measure the costs and benefits of risk treatments; these can help make a business case for risk treatments such as additional capital, investment in new controls or perhaps the purchase of insurance contracts. Without a solid business case, many risk management treatments will be left on the shelf.
It is true, however, that uncritical and unthinking use of quantitative models can be disastrous, just as unthinking use of satellite navigation systems has, on occasion, led to ‘death by GPS’. But this is not an argument for abandoning the use of quantitative models altogether, just as it would be foolish to abandon the use of GPS systems.
Even senior leaders need to have a basic understanding of quantitative methods if they’re being used to make important decisions. Without a basic understanding, how can a leader interpret reports? Or ask those searching questions? Or probe those assumptions? Thorough training in statistical methods is beyond the scope of a book such as this, but examples from several industries (aviation, import/retail, banking, resources) are used to illustrate the benefits of the approach.
To complement the extended discussion of quantitative risk, the chapter also considers subjective risk analysis. This methodology is essential in some contexts.
