When calculating a risk score is there a preference between multiplying likelihood x consequences versus adding likelihood x consequences?
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Multiplying allows for a wider range of results, which can highlight disparate risks more clearly. For example a 1 -5 scale for each factor, when added, only produces a range from 2 - 10. Multiplying allows for results to range from 1 - 25.
Multiplying likelihood by consequences ensures that a high consequence with a low likelihood, and a low consequence with a high likelihood are both appropriately weighted. For me this proportionality is crucial.
Multiplying is the most preferred method for calculating a risk score. It is more accurately and represents the combined effect of these two factors on overall risk.
When calculating a risk score, I prefer multiplying likelihood by consequences because it emphasizes scenarios where both factors are high, aligning with the intuitive understanding of risk. I believe this is a common method in risk assessments as it reflects the proportional impact of events. The catch is, if either component is zero, the risk score will be zero, which might not capture all nuances. Adding likelihood and consequences is simpler and ensures non-zero scores even if one component is zero, but it treats both factors equally, which might not always be appropriate. The choice between these methods should consider the context, scale, risk tolerance, and the audience's needs.
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When calculating a risk score is there a preference between multiplying likelihood x consequences versus adding likelihood + consequences?
Multiplication: High Probability of Success x High Reward will land in the upper right quadrant. Low Probability of Success x Low Reward will land in the lower left quadrant. This will provide a weighted average expected return when considering several combinations of risk-reward scenarios.