{"id":23000,"date":"2019-09-13T11:50:01","date_gmt":"2019-09-13T15:50:01","guid":{"rendered":"https:\/\/beyondphilosophy.com\/?p=23000"},"modified":"2019-10-21T09:58:32","modified_gmt":"2019-10-21T13:58:32","slug":"stereotypes-the-hard-wired-way-our-brains-make-decisions","status":"publish","type":"post","link":"https:\/\/beyondphilosophy.com\/stereotypes-the-hard-wired-way-our-brains-make-decisions\/","title":{"rendered":"Stereotypes: The Hard-Wired Way Our Brains Make Decisions"},"content":{"rendered":"

I’ve got a hypothetical question for you. On a scale of one to ten, how likely is it I am a male model?\u00a0<\/span><\/p>\n

Now before you destroy my self-esteem with your answer, I have a follow-up question. How did you come to that determination?\u00a0<\/span><\/p>\n

The answer to that question is the subject of this post and <\/span>a recent podcast<\/span><\/a>, and it involves the Representative Heuristic. This psychological principle could have a significant influence on how customers behave in your Customer Experience<\/a>.\u00a0<\/span><\/p>\n

<\/p>\n

So, how did you arrive at your number? Most of you probably compared me to male models you have seen in the past and determined whether I fit the profile for looks and physique. Then, based on that comparison, you assigned me a number. (It is not necessary to share your number with me, by the way\u2026in fact, I would prefer if you didn\u2019t.)<\/span><\/p>\n

This process is what decision scientists call Representativeness. Representativeness means that you have an idea of what typifies a group of people or an entity that share a characteristic. In other words, it is a stereotype. These stereotypes serve to help us categorize broad instances into a more manageable form.\u00a0<\/span><\/p>\n

The Representative Heuristic uses these stereotypes to create patterns that help us make a quicker decision in certain situations. A heuristic is a decision shortcut. I also compare it to a \u201crule of thumb.\u201d Heuristics are not 100 percent accurate, but they work most of the time, and help make complex cognitive decisions easier and faster.\u00a0<\/span><\/p>\n

 <\/p>\n

Your Heuristics vs. Our Heuristics<\/b><\/h2>\n

In decision sciences, there are individual level heuristics and universal heuristics. An example of an individual heuristic might be how you choose where you eat lunch, e.g., a place that I can walk to from my office. Another example is how you buy toothpaste, e.g., \u201cfind the one in the red package.\u201d These individual heuristics are specific to you and help you simplify your choices.<\/span><\/p>\n

Universal heuristics are broader than individual ones. These heuristics are ones we all use because of the way our brains are wired. In other words, everyone uses them all the time.\u00a0<\/span><\/p>\n

Making decisions in these ways is simpler for us, given the kind of the strengths and weaknesses of the human mind.\u00a0<\/span><\/p>\n

One of these universal heuristics is the Representativeness Heuristic, which is how we decide the likelihood that something is going to happen or that somebody belongs to some class of people.\u00a0 One way I think of this mental shortcut is if it looks like a duck and quacks like a duck and it walks like a duck, it’s probably a duck.<\/span><\/p>\n

However, decision scientists aren\u2019t interested in when the animal is a duck. They study when our mental shortcut goes wrong. So, if the animal isn\u2019t a duck but a goose, let\u2019s say. Decision-scientists want to know, where did it go wrong and what does that tell us about decision-making.\u00a0\u00a0<\/span><\/p>\n

Expectations play into it, in how you form the category or group against which you’re going to compare. For example, you’ve got expectations about how a male model looks. Therefore, when you’re pulling mental samples to form that category, it was defined by your expectations.\u00a0<\/span><\/p>\n

Somebody from a different culture or with a different set of experiences might have a completely different set of expectations.\u00a0 So, they would form a different category. Then, that would be a different Representativeness.\u00a0<\/span><\/p>\n

 <\/p>\n

The Linda Problem<\/b><\/h2>\n

Economists Amos Tversky and Nobel-prize winner Daniel Kahneman published an example of this heuristic in the 80’s. Over the years, it has become known as \u201c<\/span>The Linda Problem<\/span><\/a>.\u201d In the study, they described \u201cLinda\u201d to the participants:\u00a0<\/span><\/p>\n

Linda is 31-years-old, single, outspoken, and very bright. She majored in philosophy as a student. She was deeply concerned with issues of discrimination and social justice and also participated in anti-nuclear demonstrations.\u00a0<\/span><\/i><\/p>\n

Then, the researchers had a series of ten or twelve descriptions of Linda, including organizations that she belongs to and jobs that she does.\u00a0 They asked people to rate how probable are each of these descriptions.\u00a0<\/span><\/p>\n

However, they are only interested in two of the descriptions:<\/span><\/p>\n

    \n
  1. \u00a0Linda is a bank teller.\u00a0<\/span><\/li>\n
  2. Linda is a bank teller that is active in the feminist movement.\u00a0<\/span><\/li>\n<\/ol>\n

    So, I ask you readers, which of those two seems more probable based on the description of Linda?<\/span><\/p>\n

    I said the second one. However, it\u2019s wrong.\u00a0<\/span><\/p>\n

    It can\u2019t be more probable for her to be a feminist bank teller than it is for her to be a bank teller because of math.\u00a0 The set of feminist bank tellers is smaller than the set of all bank tellers.\u00a0<\/span><\/p>\n

    The Representative Heuristic is at work here because even though it is mathematically impossible that she belongs to the second group, it still <\/span>feels<\/span><\/i> right. Why? It feels right because Linda is so <\/span>representative <\/span><\/i>of what most of us picture as a \u201cfeminist.\u201d She doesn’t seem like she should be a bank teller. The only way we can make that work is if she’s a feminist bank teller.\u00a0<\/span><\/p>\n

    Representativeness applies to organizations also. Consider my opinion of internet companies, which, for those of you who don\u2019t know, is low. My opinion affects my expectations. If I were setting up service with an Internet company, if they were inattentive at the call center or unreliable with their service or incompetent at installation, I would think, \u201cTypical.\u201d\u00a0<\/span><\/p>\n

    However, let\u2019s say I tried satellite service instead. Here is where Representativeness comes into play. Technically, setting up satellite service would be a new experience. However, there would be parts of the interaction that would be the same, e.g., calling in to set it up, waiting for installation, trying to use the service as advertised, and so on. My brain would think, \u201cHey! This experience is like the cable provider,\u201d and then I would bring in all those associations because they were representative of that category of a service provider.\u00a0<\/span><\/p>\n

    Let\u2019s take a broader view of this concept. Consider your experience with a sales-driven organization. You know when you are in one because the customer-facing people are aggressive and usually hard to shake.\u00a0<\/span><\/p>\n

    As a rule of thumb (see what I did there?), I don\u2019t give my information to sales-driven organizations easily. I tend to hold back information rather than lay it all out until I determine that I want to move to the next step. My behavior is governed by what I think a sales-driven organization is going to be like based on my expectations.\u00a0<\/span><\/p>\n

    People use these probability judgments to make all kinds of decisions, and they go right most of the time. However, let me give you a specific example of how these judgments go wrong, and it has to do with neglecting base rates. When you are discussing probabilities, base rates are the likelihood that something will be accurate based on featural evidence.\u00a0\u00a0<\/span><\/p>\n

    In the Linda problem, the group of bank tellers is larger than the number of feminist bank tellers. For demonstration, let\u2019s say 99 percent of bank tellers are not feminists, and 1 percent are. That means the base rate of feminist bank tellers is 1 percent.\u00a0<\/span><\/p>\n

    We neglect base rates in the extremes and that\u2019s where Representativeness goes wrong for us. For example, we overlooked the base rate of feminist bank tellers. We forgot that the number of bank tellers was larger than the number of feminist bank tellers.\u00a0<\/span><\/p>\n

    Another example could be a high school basketball player that you see who is fantastic, and the player represents your idea of professional players. You think, \u201cthat kid is going pro, for sure.\u201d However, per the NCAA, the base rate for high school players playing professional basketball is <\/span>1.2 percent for men and .9 percent for women<\/span><\/a>. So, even though the kid is excellent and might represent what you expect for a professional player, they probably won\u2019t go pro. Again, because of math.<\/span><\/p>\n

    If you put this concept into a business context, it can mean that as a customer, you make the same wrong assumptions about an organization.\u00a0 For example, if you engage with a company that is representative of some class of companies that you don’t like, you might overestimate the likelihood of a service failure. The opposite could be true also. The danger is that you tar everyone with the same brush and make the wrong assumption.\u00a0<\/span><\/p>\n

     <\/p>\n

    How Can You Use This Concept?<\/b><\/h2>\n

    There’s a reason that the scientists study the Representativeness Heuristic: it is a reliable driver of behavior. The Representative Heuristic is hard-wired into our brains, making it difficult to overcome.\u00a0<\/span><\/p>\n

    However, by maintaining rational-thinking about base rates, you can overcome it. We need to have an evidence-based probability for success or failure. Otherwise, we don\u2019t have a sound foundation for making an estimate. We could be way off because we aren\u2019t looking at the situation clearly enough.\u00a0<\/span><\/p>\n

    Your customers also make estimates that are way off. We have to understand where they are coming from and exercise patience with them in their decision-making. For example, if you are suffering from an overestimated sense of probable failure based on the category of business you represent in customer\u2019s minds, it will take a long time to change customers\u2019 minds.\u00a0<\/span><\/p>\n

    Your goal, then, should be to consistently play against type and prove to customers that you are not what they thought you were. Your Customer Experience<\/a> should surprise them and make them feel differently than they expected.\u00a0<\/span><\/p>\n

    To address this implication in your Customer Experience, you should:<\/span><\/p>\n