The devil is in the details.

The devil is in the details.

Sep 21st, 2012

“The devil is in the details.” explore what shortcuts in risk management leads to exposures and potential liabilities.

We all love rounding numbers, i.e. “erasing the decimals” by pushing the value up or down to the nearest, or more pleasing, integer.
When we let our “esthetics sense”, or our will to influence our audience, dominate, we will merrily jump way more than the decimals and come out with “media friendly rounded” values such as “one million people came to the streets”.

The devil is in the details.

Often the nicest numbers will also be the ones the farthest from reality.
Many risk assessments, especially if they are used to obtain approval of a project, or an alternative selection, will use the esthetic art of biasing reality with artificial, misrepresenting rounding.

How is it done?
Well, these are examples of great classic approaches:

1) they start by saying that because no one can know what will happen in the future (who knows, a meteorite could fall on our head…), they will limit their analysis to credible (they do not define credible, of course), average consequence (they do not define average, of course). By doing that, they avoid bringing into the analyses all sorts of embarrassing high likelihood/low-consequence and low likelihood/catastrophic-consequence scenarios (Fukushima anyone? Or BP).

2) The second step is to divide reality in discrete classes, let’s say five “steps” of increasing likelihood and consequence, with carefully selected arbitrary limits of the classes. I oftentimes see risk assessments depicting a biased reality to fit the purposes of misrepresentation.

3) The third step consists in making everyone to believe that for each (risk) scenario it is perfectly OK to forget any kind of variability. Thus it is ok to “stick” a scneario in a cell based on “rounded” value of likelihood and consequence. At this point many decide to go even further away from reality by using indexes (like 1-5, talk about rounding!) instead than “real” numbers.

4) Finally, another arbitrary set of assumptions leads to draw “stepped” limits splitting the cells in “rounded” categories driving attention level and mitigation decisions.
I will let our readers decide how far from reality such an assessment can be and how wrong the resulting decision making could be, especially if the project/alternative is not a usual one.

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Category: Consequences, Hazard, Optimum Risk Estimates, Probabilities, Probability Impact Graphs, Risk analysis, Risk management

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