56 - Using the Area Under the Curve Framework to categorize risk

06-05-2023

decision-making

All day, every day, you’re being bombarded with problems that need solving. Here’s a simple framework that’ll help put things in perspective so that you don’t lose sleep over an ant bite or sleep through a snake bite.

I propose a general purpose framework that I call Area Under the Curve. It’s got two dimensions–how big a problem appears to be and how often it seems to occur–and you can use it to rank problems and plan a response.

Imagine a 2X2 matrix with Problem Size and Problem Frequency as the vertical and horizontal axes. Let’s plot some examples.

Low size-low frequency: A malfunctioning gadget

Low size-high frequency: A long commute

High size-low frequency: A job change

High size-high frequency: A toxic personal relationship*

*You don’t get yourself into bad relationships repeatedly but let’s say you’re in one where bad interactions happen repeatedly.

The bigger the area under the curve, the more attention you need to give it.

What’s new here, you may ask. Well, if you’ve ever posted anything online to generally good reviews and then you find yourself fixated over that one negative comment, you would know your internal apparatus is fallible. So you need a system to safeguard against situations when you may go from logical to psycho-logical.

Here are typical scenarios where we misread either problem size or frequency.

Underestimate problem size (in fat tailed distributions): health. Because these are fat-tailed problems, which means that there’s a likelihood, however small, of disasters. But we tend to ignore that part. Think about chronic diseases and ask if your life is designed to avoid them.

Overestimate problem size (in normal distributions): salary. There are several things that determine your happiness at work, but you could focus all your attention on moving this one number up. But even in the best case, it’ll only move so much for reasons of parity.

Underestimate problem frequency (common inconveniences): traffic. People visiting Mumbai are often taken aback by the traffic. Locals simply get on, like after a mosquito bite. Yet, what animal kills the most humans every year? The mosquito.

Overestimate problem frequency (the odd unpleasant emotional experience): terrorism. It may not be at your doorstep but thanks to availability bias it’s what you think and talk about disproportionately.

Once you plot problem size and problem frequency on the AUC matrix, you can think of how to tackle them: plan for, prioritize, control, or ignore.

If you really _want _to tackle a problem or not, you’re likely to find a way to justify it. What the Area Under the Curve framework does is slow that decision-making process down so that your rational mind has a say.

Coming to risk, we can define it as:

Risk = Negative outcome X Likelihood of it happening

Here again we can use the AUC method to calculate risk. But with one switch: change frequency to probability.

Haley’s comet appears once in 76 years. Likelihood of an average person seeing a Haley’s comet in their lifetime–low

Olympics happen once every 4 years. Likelihood of an average person seeing an edition of the Olympics in their lifetime–very high.

In evaluating risk, there are two combination scenarios where our behavior is noteworthy.

One: Negative outcome is low (minor inconvenience); likelihood of happening is high (daily, hourly)

Two: Negative outcome is high (threat to life); likelihood is low (rare thing)

We take steps to control the first scenario from mosquitoes to traffic. But it is in the second scenario that we respond to uncertainty emotionally in a way math can’t predict.

For example, imagine there’s a 99% chance that a random plant is not poisonous. But if you were to be asked to eat it, you most certainly would not even though you believe that the chances of a negative reaction are rather slim (1%). But the consequences of the outcome are significant enough for you to focus on the size parameter and ignore the likelihood parameter.

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