Correctly assessing risks – or how we ignore the denominator

In general, people are not very well equipped to correctly assess a risk. This is because we ignore the denominator in the fraction and only rely on the numerator. Thus we overestimate the chance of dying from coronavirus. Of course, an additional difficulty with Covid-19 is that we do not have an accurate idea of the denominator – how many people in total have been infected – due to a lack of testing. But alongside this, there are a number of characteristically human mindsets that further accentuate this denominator neglect.

In behavioural economics, these thinking patterns or mechanisms are often described as the result of System 1, our quick way of thinking, whilst we would often be better advised to use System 2, our slow way of thinking.

1) The law of small numbers
Because we do not have access to more observations, we frequently allow ourselves to be guided by a small number of observations from our immediate environment. And small samples are more likely to deliver a striking result than larger samples. Either there is currently no one in your immediate environment who is sick and you feel untouchable, or you are aware of someone who has died, and this has really affected you, making you extremely scared.

2) We use the availability heuristic
We are easily guided by information and examples that are easy to remember. We all know someone who has died of breast cancer, whilst it is far more difficult to think of someone who has died as a result of air pollution. Because it ‘feels’ easy to come up with these examples, we base our perception of risk on that feeling (cognitive ease) with which we can come up with examples. This can also lead to an availability cascade: a meaningless event that is blown up by the media until it is the only thing being discussed.

3) Affect heuristic
We not only use the feeling of cognitive ease with which we can come up with examples, but also how we feel about it. If we become sad or afraid as a result, we use this emotion to assess risk. In so doing, we are actually engaged in question substitution: “How committed are you to saving the lives of corona patients?” then becomes “What do I feel when I think of a dying corona patient?”.

4) Causes take precedence over statistics
We like to be guided by the causes of individual cases, rather than statistical likelihoods. For example, there are currently hopeful figures suggesting that many human lives have been saved during lockdown by better air quality, but people do not really take this into account. Perhaps the global statistical likelihood of you dying as a result of air pollution is also greater than your likelihood of dying from Covid-19. Yet poor air quality as a factor is too vague and too abstract to be perceived as a cause of death. Corona is a more concrete cause, which you can vividly call to mind, and which therefore trumps the statistics.

Can experts do this better? The literature is not yet in agreement about this. But the fact is that people with a statistical background use these heuristics to a lesser extent. So above all, we should not ignore this subject in these times of online education. And we should argue for behavioural economists to be included in the panels of experts who are guiding our policymakers, both now and in the future.

How is this applicable to the business world? Well, the same mechanisms are at work when entrepreneurs try to assess their risks as a start-up or on the stock market. For example, when assessing a start-up’s chances of success, base-rate odds of failure in a certain sector often go unheeded, and are supplanted by success stories and examples that appeal to the imagination. Thus each of the aforementioned thinking patterns can result in the denominator – the total number of start-ups that have ever attempted it – not being taken into consideration. If the odds of failure in a particular sector are 6 out of 10, for example, there is little point in not taking this information into account, and in only being guided by hope, based on the success of the Googles of this world. This would seem to be a pessimistic conclusion, but a statistical mindset can also help us to maintain a rational and realistic assessment of a business.

Source: Kahneman, D. (2011). Thinking, Fast and Slow (Kindle Edition).

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