representativeness-heuristic

Representativeness Heuristic In A Nutshell

The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.

Understanding the representativeness heuristic

They noted that the representativeness heuristic explains the degree to which an event is:

  1. Similar in essential characteristics to the parent population (class), and
  2. Reflective of the important features of the process by which it is generated.

To better explain the heuristic, consider the example of John.

John is a history buff who enjoys visiting museums and other places of cultural significance. He is also a regional chess champion and goes fossicking for gold on the weekend.

Given the information supplied, which is the more likely scenario?

  1. John is an archaeologist in residence for a prestigious university.
  2. John is a truck driver.

When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.

However, the odds that John is a truck driver are far greater because truck drivers make up a higher percentage of the population than archaeologists.

When decisions are made based on the representativeness heuristic, the individual is more likely to overestimate the likelihood of an event occurring. For a given event, there is no correlation between representativeness and a higher probability of that event occurring.

The representativeness heuristic in business and marketing

The representativeness heuristic is common in consumer behavior because products are rarely described completely. As a result, the consumer must form inferences about the information that is missing.

In a 2004 study, researchers found that consumers inferred a higher product quality from a no-name brand if the packaging was designed to mimic a better known global brand.

Representativeness is also seen in finance where investors prefer to buy a stock with unusually high share price appreciation. Further studies demonstrated that investors misattributed positive company characteristics (such as high-quality products) as an indicator of a good investment.

Applications in marketing

Marketing agencies use the heuristic to convince consumers that products are representative of ideas or concepts they already possess.

Advertisements depicting suave men drinking alcoholic beverages surrounded by women lead consumers into thinking that they must also drink that brand to be popular with the opposite sex.

Marketing campaigns for SUVs and trucks also suggest that their rugged off-road vehicles are only driven by similarly rugged men. 

In each case, the consumer makes a buying decision based on comparing their current situation to a representative example.

Key takeaways

  • The representativeness heuristic occurs when individuals estimate the likelihood of an event based on a broad and typical example of an event or object.
  • The representativeness heuristic causes the individual to overestimate the chances of an event occurring. This is caused by incorrectly correlating representativeness with higher probability.
  • The representativeness heuristic is prevalent in marketing campaigns where product qualities, concepts, or themes are matched with those the consumer believes they already possess.

Related Case Studies

heuristic
As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.
recognition-heuristic
The recognition heuristic is a psychological model of judgment and decision making. It is part of a suite of simple and economical heuristics proposed by psychologists Daniel Goldstein and Gerd Gigerenzer. The recognition heuristic argues that inferences are made about an object based on whether it is recognized or not.
representativeness-heuristic
The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.
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The take-the-best heuristic is a decision-making shortcut that helps an individual choose between several alternatives. The take-the-best (TTB) heuristic decides between two or more alternatives based on a single good attribute, otherwise known as a cue. In the process, less desirable attributes are ignored.
biases
The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.
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The bundling bias is a cognitive bias in e-commerce where a consumer tends not to use all of the products bought as a group, or bundle. Bundling occurs when individual products or services are sold together as a bundle. Common examples are tickets and experiences. The bundling bias dictates that consumers are less likely to use each item in the bundle. This means that the value of the bundle and indeed the value of each item in the bundle is decreased.
barnum-effect
The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.

Read Next: Heuristics, Biases.

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