Another interesting heuristic that deserves some attention is the representativeness heuristic. This heuristic helps us estimate probabilities through the following simple rule: prototyping events are likely events.

The astronaut that wasn’t an astronaut

To illustrate this heuristic all describe a person let’s call her Rebecca and you should think about what sort of job she might have. Here’s the description: Rebecca is intelligent extroverted and gregarious, from an early age she always liked looking at the night sky and speculating about life on other planets, in school she was always strong in math and science courses although she was a good writer as well.

As an adult Rebecca loves going fast, running riding horses and even driving cars are all passions, she is willing to take risks especially if they allow her to pursue her dreams and she’s a good communicator especially for thing she feels passionate about. So how likely is it that Rebecca is a, say, teacher? just think of a rough percentage. What about an astronaut? A lawyer? A science writer?

I adapted this example from the work of Daniel Kahneman and Amos Tversky, two pioneering figures in behavioral economics. When people hear scenarios like this, they commonly report the Rebecca’s most probably an astronaut. She seems like a prototypical astronaut, she’s interested in science and math, she has a love for the night sky and life on other planets, she’s willing to take risks and passionate about her work.

Next most probable would be science writer, she loves science and is a good writer communicator after all and least least probable would be lawyer and teacher. Now think for a moment. Have you ever met an astronaut? Do you have an astronaut living on your street? At any one time the United States has about 100 people whose current job description is astronaut.

That’s it. By comparison, there are several million teachers, more than a million lawyers and thousands of science writers. Even if Rebecca seems most representative of an astronaut, there are so few astronauts that it is much more likely that she’s a teacher or lawyer. There are surely many more science oriented and risk loving teachers and lawyers than there are astronauts.

Representativeness fails when prototypes are rare

The representativeness heuristic fails here because these professions differ dramatically in their base rates, teachers and lawyers are much much more common than science writers and especially astronauts. So why do we rely on representativeness when it leads to such obvious mistakes? This heuristic exist because it is a fast and simple guide to probability.

If it’s a beautiful clear morning it’s unlikely to rein in the afternoon, students with good grades are more likely to do well in medical school, the leading brand of toothpaste is a good product. Prototypes are often prototypes for reason, they are often the best or most common member of some category. So representativeness fails when prototypes are rare like astronauts but succeeds when prototypes are common.

That rule provides clear guidance: be wary when representativeness point you toward some rare conclusion. To paraphrase the old saying the medical students when you hear hoof beats behind you expect horses not zebras. Which brings us to another interesting concept: the affect heuristic. The affect heuristic involves choosing one option over another based on their anticipated effects on our emotional state, and about this in a different article.