Four of the most important heuristics, which providing a tool for overcoming a particular sort of cognitive limitation, are: the familiarity heuristic – which involves memory, the anchoring heuristic – which involves valuation and reference points,  the representativeness heuristic – which involves the estimation of probabilities and the affect heuristic – which involves simulation of feelings and emotions.

The familiarity heuristic

By familiarity I specifically mean that we recognize something. The familiarity heuristic makes brand-name consumer products more desirable, makes violent crime scene more common than they really are and made the casual football fan choose a Steelers over the Packers in our opening example. Familiarity comes from the ease with which something is available to be brought to mind and thus it is sometimes called the availability heuristic.

In one study familiarity researchers in Germany tested German students on simple questions about the relative populations of cities in the United States, questions like which is larger Philadelphia or Lubbock Texas. They also tested cities in Germany which are larger Frankfurt per Essen. Obviously the German students knew much more about cities in Germany than about cities in the United States But they actually got a higher proportion of questions correct when asked about the US cities. How can that be possible?

Familiarity develops because of structure

To understand why, let’s think for a moment about what familiarity means. Most of us are familiar with New York City New York but not with New Bern North Carolina. We are familiar with the writing of Charles Dickens but not necessarily that of Crockett Johnson and we are familiar with elephants but not with penguins. But, what makes us more familiar with New York compared to New Bern?

New York is populous, a world hub for commerce and information and a constant presence in the news media. You’ve been exposed to New York City in one way or another thousands of times in your life. New Bern is a former capital of North Carolina in the birthplace of Pepsi-Cola. It’s a charming small town but unless you had a specific reason to go there you may never have heard of it. Let me emphasize this: our sense of familiarity develops because of the structure in the world around us.

More familiar, a better choice

Large cities tend to be culturally or economically significant and they are more likely to be familiar. So let’s think about those German students, when they were asked questions about cities in Germany they were familiar with all of the cities. Familiarity wasn’t a useful clue as to which one was larger, but when the same students were asked about US cities then suddenly their sense of familiarity became a good guide to population.

Which is larger Philadelphia Pennsylvania or Lubbock Texas? That’s an easy question for someone from Germany. The familiar city is the larger one, and familiarity turns out to be a wonderful guide for answering all sorts of similar questions. Which mammal lives longer? an elephant or a pangolin? When researchers ask people questions like this one people usually just chose the animal that was familiar to them and they were right most of the time. We are more familiar with larger longer lived animals and so our sense of familiarity again track something meaningful.

A powerful tool because of the structure in the world

The decision scientist Gerd Gigerenzer has shown that being too familiar with what’s being judged can actually undermine the benefits of familiarity. When making judgments analogous to the previous examples which city is larger which authors sold more books Gigerenzer and colleagues show that the most accurate judgments would be made by someone whose only familiar with about 75% of the items being judged. That person would typically do better than someone who is at least heard of every item before.

Familiarity provides such a powerful tool because of the structure in the world around us. Let me give you another example. If you ask casual sports fans to predict the outcomes of Wimbledon tennis matches they often simply pick the player with whom they are familiar. These simple judgments of familiarity have been shown to be good predictors of who will win a match and even do slightly better than the actual player rankings.

Judgments of probability

The familiarity heuristic is also connected toward judgments of probability. Remember, people tend to overestimate small probabilities but underestimate large probabilities. This can lead to a strange phenomenon called sub additivity. Think about a 50-year-old man of the United States will call him Stephen. Stephen has smoke for much of his adult life always trying to quit, he’s in generally good health despite a diet high in fatty foods in a largely sedentary lifestyle, he does have a family history of heart attack and stroke. I give this sort of example in class and ask my students how likely is Stephen to die a heart attack, a stroke of heart, disease of lung cancer of brain cancer melanoma and of a few other diseases.

A typical student might guess that Stephen has a 25% chance to die of a heart attack, a 50% chance of a stroke, a 30% chance of heart disease, a 25% chance of lung cancer, a 20% chance of brain cancer and so on. Then I ask the students to add up the probabilities. The students usually laugh because they just estimated Stephen has something like 150% chance of dying from these different causes. The students can readily link Stephen smoking to lung cancer or his diet to heart disease, so when they think about each cause of death in isolation, is judged to be more likely than it should be. That’s sub-additivity, the total probability which must be 100% ends up less than the sum of the individual probabilities.

Familiarity heuristic affects our economic decisions

The familiarity heuristic affects our economic decisions too: people overvalue and are more likely to invest in the stocks of familiar companies. This can lead to very bad outcomes: in the extreme investing one’s retirement savings in one’s own company stock carries a massive risk, for example catastrophic event that bankrupted the company could also eliminate all retirement savings.

But there is other evidence from behavioral finance that investors whose portfolios have a bias toward familiar companies such as those from one’s own geographic area obtain slightly higher returns than investors who show no familiarity bias. Familiarity involves the use of information and that information might help some investors. Now let me move to the second heuristic: anchoring. This heuristic uses some initial estimate as an anchor to bias or subsequent judgments. But, about this in a different article.