Unraveling the Illusions of Quick Judgment: Navigating the Maze of Representativeness.

A Nerdy Morning Read

This morning, over a hot cup of coffee, I poured through a 1970s article by psychologists Daniel Kahneman and Amos Tversky. The article, titled “Judgments Of and By Representativeness,” reveals how humans often rely on representativeness, to what I consider, scary levels.

Understanding Representativeness

Representativeness is a mental shortcut, assessing how similar something is to our typical idea of a category. For example, most people would think a Robin is more representative of a bird than a chicken. It’s intuitive, yet misleading. Chickens are far more common.

We do this regarding a lot of things, not just birds. Take this example: meeting a quiet, book-loving teenager with glasses, you might immediately think, “future librarian.” This judgment isn’t based on statistical data but on how representative the person feels of the category "librarian"

The Deception of Our Intuition

Oddly, our brain doesn’t see the mathematical improbability unless some great effort is purposefully directed at it. We just connect "A", in this instance the girl, being typical of "B", in this instance the stereotype held of librarians. This results in the conclusion that B is a highly probable outcome for A, a process known as subjective probability.

Real-World Consequences of Cognitive Bias

Ok – this is nerdy stuff. Why care? There are significant real-world consequences to this bias. In a job interview, a candidate who looks professional and presents well can easily lead us to think he or she will be a successful employee. But, does appearance really relate to job performance? What about a police officer eyeing someone suspiciously based on appearance. He pulls up and interrogates the individual in an unjustified manner. Humans just do this. It’s called representativeness, and our minds build narratives around it often. Concerned yet?

The Storytelling Nature of Humans

See, humans are natural storytellers. The job interviewer imagines a narrative of success; the police officer one of criminality. Extend this out and you think of marketers, jurors, business leaders, even you and me. We construct these stories to explain patterns, oblivious to our overlooking the complete lack of actual causal relationships

Don't believe me? Test yourself:

John is 27, outgoing, and was an athlete in college with little intellectual interest. Is it more probable that John is a gym teacher or a teacher? Congratulations if you answered “gym teacher”. That’s the most common answer. But logically, this is less probable since gym teachers are a subset of teachers. Our brains are wired in a way that simply prefers the stereotype over statistical probability because we love stories. Our minds go to it. Not as much for mathematics, it appears.

And when polling on Twitter, 2/3 of all respondents relied on a cognitive bias rather than rational probability in their own answers.

The relevance to Causation

Humans create causal relationships when there are none. Ask a group of marketers why their consumers have begun to purchase less of their products. Ask a university why it is seeing an increase of foreign student enrollments from a given country. Ask a trader why a stock is going up. In each of these situations – there are people being asked to explain cause in the absence of causal data. Yet, we feel a need to answer, A human, and at times professional, obligation. And so we do.

We unconsciously rely on our cognitive bias to determine causality. Sounding like our new friend, "Subjective Probability", yet"? Since our minds like stories, we begin crafting them, sometimes adding details which actually reduces the statistical likelihood for accuracy. Like adding “gym” to “teacher” because our minds liked that story more. When it comes to our quest for causality, we craft narratives that match as closely as possible to our model of what is typical. Often abandoning accuracy in the process.

A Pragmatic Approach in Decision-Making

So, next time you are in a meeting and being presented with new information on consumer behavior or student mobility or stock price, be wary of jumping to conclusions based on representativeness. Without solid data guiding you, any conclusions made on causation is likely riddled with cognitive bias and will prove to be misleading. & No one wants to be misleading when it comes to work or financial markets. Those outcomes don’t bode well for us.

Instead – skip the need for causation. Don’t ask, “why?”. Ask “what are we going to do about it?” Such a pragmatic approach will lead to greater success and less bias. In recognizing the pitfalls of representativeness, we can shift from crafting narratives to making decisions based on objective data and practical strategies. This approach doesn't just lead to more accurate judgments; it propels us towards more effective actions.