The representativeness heuristic was coined by Daniel Kahneman and Amos Tversky, two of the most influential figures in behavioral economics. The classic example used to illustrate this bias asks the reader to consider Steve, whom an acquaintance has described as “very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.” After reading a description of Steve, do you think it’s more likely that Steve is a librarian, or a farmer? 2 Intuitively, most of us feel like Steve must be a librarian because he’s more representative of our image of a librarian than he is our image of a farmer.
As with all cognitive biases and heuristics, there is one main reason we rely on representativeness so often: we have limited cognitive resources. Every day, we make thousands of separate decisions, and our brains are wired to do so while conserving as much energy as possible. This means we often rely on shortcuts to make quick judgments about the world. However, there is another major reason that the representativeness heuristic happens. It is rooted in the fundamental way that we perceive and understand people and objects.
We draw on prototypes to make decisions
Grouping similar things together—that is, categorizing them—is an essential part of how we make sense of the world. This might seem like a no-brainer, but categories are more fundamental to our ability to function than many people realize. Think of all the things you are likely to encounter in a single day. Whenever we interact with people, objects, or animals, we draw on the knowledge we’ve learned about their category so that we can know what to do. When you go to a dog park, for example, you might see animals in a huge range of shapes, sizes, and colors, but because you can categorize them all as “dog,” you immediately know roughly what to expect from them: that they like to run and chase things, that they like getting treats, and that if one of them starts growling, you should probably back away.
Without categories, every time we encountered something new, we would have to learn from scratch what it was and how it worked—not to mention the fact that storing so much information about every separate entity would be impossible, giving our limited cognitive capacity. Our ability to understand and remember things about the world relies on categorization. On the flip side, the way we have learned to categorize things can also affect how we perceive them.3 For example, in Russian, lighter and darker shades of blue have different names (“goluboy” and “siniy,” respectively), whereas, in English, both are referred to as “blue.” Research has shown that this difference in categorization affects how people see the color blue: Russian speakers are faster at discriminating between light and dark blues, compared to English speakers.4
According to one theory of categorization, known as prototype theory, people use unconscious mental statistics to figure out what the “average” member of a category looks like. When we are trying to make decisions about unfamiliar things or people, we refer to this average—the prototype—as a representative example of the entire category. There is some interesting evidence to support the idea that humans are somehow able to compute “average” category members like this. For instance, people tend to find faces more attractive the closer they are to the “average” face, as generated by a computer.5
Prototypes guide our guesses about probability, like in the example above about Steve and his profession. Our prototype for librarians is probably somebody who resembles Steve quite closely—shy, neat, and nerdy—while our prototype for farmers is probably somebody more muscular, more down-to-earth, and probably less timid. Intuitively, we feel like Steve must be a librarian because we are bound to think in terms of categories and averages.
We overestimate the importance of similarity
The problem with the representativeness heuristic is that representativeness doesn’t actually have anything to do with probability—and yet, we put more value on it than we do on information that is relevant. One such type of information is prior probability or base rates: how common something is in general. For instance, at least in the U.S., there are many more farmers than there are librarians. This means that in statistical terms, it would always be incorrect to say Steve is “more likely” to be a librarian, no matter what his personality is like or how he presents himself.2
Sample size is another useful type of information that we often neglect. When we are trying to make estimates about a large population, based on data from a smaller sample, we want our sample to be as large as possible, because then we have a more complete picture. But when we focus too much on representativeness, sample size can end up being crowded out.
To illustrate this, imagine a jar filled with balls. ⅔ of the balls are one color, while ⅓ are another color. Sally draws 5 balls from the jar, of which 4 are red and 1 is white. James draws 20 balls, of which 12 are red and 8 are white. Between Sally and James, who should feel more confident that the balls in the jar are ⅔ red and ⅓ white?
Most people say Sally has better odds of being right because the proportion of red balls she drew is larger than the proportion drawn by James. But this is incorrect: James drew 20 balls, much greater than Sally’s 5, so he is in a better position to judge the contents of the jar. We are intuitively tempted to go for Sally’s 4:1 sample is because it is more representative of the ratio we’re looking for than James’ 12:8, but this leads us to an error in our judgment.