Normalcy Bias

What is it: we habitually underestimate the chances of threats and the likelihood of disastrous outcomes…

“There has never been a problem, so I hear what you’re saying, but I don’t believe there could be a problem…”

Overview

The Normalcy bias, or normality bias, causes people to not heed warning of, or minimise the likelihood of, potentially disastrous outcomes. Individuals therefore underestimate the chances of a disaster, or adverse effects, instead believing things will simply continue as is.

Everyone hopes that the normal way of working will continue to, well, just work! The idea the things can and will change is scary to us, so like a lot of things we don’t like, we instead persuade ourselves there is no risk, and when we don’t believe there is a risk, we don’t plan for one.

Hoping for the best, but planning for the worst, is good practice, however, when we perceive the chances of a catastrophic event, we simply don’t think it’s worth it.

This manifests itself in many scenarios; natural disasters, travel disasters, freak weather, illness and financial markets.

One of the most famous examples was the Titanic. Why were there so few lifeboats? It was simply because the ship’s architects and builders couldn’t conceive that such a catastrophic incident could occur. It also explains why the inhabitants of Pompeii apparently sat watching Vesuvius for hours before realising they were in mortal danger. In that scenario though, poor communication and education meant most just couldn’t imagine a catastrophic outcome, they had no point of reference.

Whilst there is normalcy bias, believing things will continue as-is, there is also the opposite too; the over-reaction bias, where we act disproportionately to perceived threats.

We have seen this over the last three years with Covid. When Covid first struck, many strongly believed the worst-case scenario predictions and we acted as such.

What can we do to avoid this?

Understanding statistics is important. Understanding topics like regression to mean, will help you understand peaks, troughs and trends. It’s a glib point but, as explained above, hope for the best and fear for the worst.

Look too at the gambler’s fallacy. With this bias, we tend to believe that luck will even itself out. If a lottery number has come up four or fives times in a row, we believe it more likely that the run will end soon? Does the lottery ball picker know a number has come up five times in a row? No. So, the chances of it coming up again a sixth time, are just the same. Similarly, but conversely, we believe the trends are more likely to continue because, that’s the ‘normal’ way. Just because something has happened, or not happened for a long time, it doesn’t mean it won’t change, or regress to the mean.

Examples

• In the US, Congress has just cleared the Boeing 737 Max 10 jet for certification without further modifications and safety enhancements. That’s despite two deadly crashes in 2019. There has been a severe backlash from the 15,000 pilots at American Airlines, and from the chair of the House Transportation Committee that led the key congressional investigation into the MAX crashes.
• Many investors didn’t plan for a financial crisis such as that in 2008, because they looked at recent stock market gains and refused to believe that as-is wouldn’t continue. That crash was caused in part from a belief that mortgage payments were always met, defaults were traditionally low and risk was low. Nobody believed it would crash, so nobody went looking for any evidence. There was though, a healthy helping of confirmation bias too.
• Although we had modelled for, and practiced responses to, the Covid-19 pandemic, the fact that there hadn’t been a severe pandemic since the 1918 Spanish Flu pandemic (there had been Avian Flu and Swine Flu but these were well contained), we assumed it was very unlikely to happen. When Covid-19 first appeared, many also continued as-is, simply refusing to be believe it could be serious – remember Boris Johnson shaking the hands of patients in hospitals?! This has been seen in many disasters.