How do you prevent false intuitive judgement? - Chapter 14

Imagine drawing one ball from a jar. To determine whether the ball is more likely to be black or yellow, you need to know how many balls of each color there are in the jar. The proportion of balls of a specific color is called a ‘base rate’.

If you have to guess the graduate specialization of university student Patrick by ranking a list of fields in order of likelihood, you quickly realize that the relative size of enrollment in these field is important. Due to the absence of more information about Patrick, you will go by the base rates and predict that Patrick is more likely to be enrolled in law, than in medicine or art history, because there are more students overall in law. We use base-rate information when there is no further information.

Now you are asked to read a description about Patrick’s personality. “He is smart, socially skilled, a great listener and he cares for others. He is very capable of taking decisions under pressure and working in a team. He is responsible, disciplined and committed. His handwriting is terrible.” Again, you are asked to guess the specialization of Patrick from a list of fields. This question requires retrieving a stereotype of graduate students in the fields mentioned. The description fits the stereotype of a medical student.

The task of ranking the fields is hard and requires organization and discipline, which are operations of System 2. The clues used in the description activated an association with a stereotype, which is an automatic operation of System 1. The accuracy of the description and knowing the base rates of the fields are irrelevant. Focusing exclusively on the similarity of someone’s description to stereotypes is called ‘representativeness’.

The description of Patrick causes a clash between base rates and representativeness. When a statistic was asked to carry out similar tasks and guess Patrick’s profession, he answered with ‘medical doctor’. He neglected the relevance of the base rates after reading the description. An experiment among psychology students also resulted into substitution: the easier question about similarity (judgment of representativeness) substituted the difficult question about probability. Ignoring base rates and not paying attention to the accuracy of evidence in probability tasks will certainly lead to serious mistakes.

Statisticians and logicians disagree about the meaning of ‘probability’. Many consider it a measure of subjective degree of belief. Both have formulated a competing, precise definition of ‘probability’. For non-experts, it is a vague notion, also known as ‘likelihood’. It is associated with plausibility, surprise and uncertainty. We more or less know what we mean when we use the word, so it is not troublesome. Questions about likelihood or probability activate a mental shotgun: evoking answers to less difficult questions. An example of an easy answer is the automatic assessment of representativeness. System 1 unintentionally produces an impression of similarity.

Other examples of the representativeness heuristic are “She won’t become a good doctor with all those piercings’ or ‘He will receive the most votes, you can see he is a great leader.”  Although it occurs often, predictions by representativeness are not statistically optimal, which is demonstrated in the following story. Sport scouts are prone to predict the potential success of players by their look and build. The manager of a club overruled the scouts by selecting players on the basis of statistics of previous performances. The players he choose were less expensive, but performed excellently.

What are considered sins of representativeness?

Intuitive impressions produced by representativeness are usually more accurate than a random guess would be. A person who acts friendly usually is friendly. In most cases, there is some truth to a stereotype. In other cases, the stereotypes are wrong and the representativeness heuristic is misleading, particularly if it causes the neglect of contradictory base-rate information. Relying merely on the heuristic, even if it is somewhat valid, goes against statistical logic. The exorbitant willingness to predict the occurrence of low base-rate (unlikely) events is considered a sin of representativeness.

Imagine seeing a man reading The Financial Times in a London park. Which of the following statements is a better guess about the reading man? “He has a PhD” “He does not have a college degree”. Representativeness makes you willing to go for the first statement, but the second statement should be seriously considered, because a lot more non-graduates visit parks.

Base-rate information will not always be neglected when more information about the topic is available. Research shows that many people are influenced by explicitly provided base-rate information, although the information about the specific case normally trumps mere statistics.

System 1 and System 2 are both to blame when a false intuitive judgement is made. System 1 generated the intuition, while System 2 validated it and expressed it in the form of a judgment. System 2 fails due to either laziness or ignorance. Base rates get ignored by some people because individual information is available (deeming it irrelevant), while other people are not focused on the task (laziness).

Insensitivity to the quality of evidence is another sin of representativeness. This is related to the WYSIATI-rule of System 1. The description of Patrick activated your associative machinery, but it may not be accurate. Even if you were explicitly told the statement is not very trustworthy, some parts were convincing enough. System 1 automatically processes the available information as if it were the truth, unless it immediately rejects it (for instance because it came from someone you don’t trust). When you are doubting the quality of the evidence, you should let your probability judgment stay near the base rate, which is an effortful exercise of discipline. The right answer to the student Patrick puzzle is staying close to your initial beliefs, slightly reducing the high probabilities of well-enrolled study fields and slightly raising the low probabilities of rare fields. The little evidence you received about Patrick should not be trusted, so your estimation must be dominated by the base rates.

How can you discipline intuition?

Your expectation that it will snow tomorrow is your subjective degree of believe. You should stop yourself from believing anything that comes to mind: discipline your intuition. The logic of probability should constrain your beliefs. If you believe there is a 70% chance of snow, you must also believe that there is a 30% chance it will not snow and not believe that there is a 20% chance of snow.

The rules for puzzles like the student Patrick one follow from Bayesian statistics. The rule of Thomas Bayes specifies how initial beliefs (for example, base rates) should be combined with evidence diagnosticity. Two ideas are important to remember: base rates matter and intuitive judgments of the evidence diagnosticity are frequently exaggerated. The combination of associative coherence and WYSIATI has the tendency to make us believe our own fabricated stories. You can discipline your intuition the Bayesian way by:

• Anchoring your judgment of the probability of an outcome on a plausible base rate

• Questioning the diagnosticity of the evidence.