Representative Heuristics — Making Judgments Under Uncertainty

When faced with uncertainty, people often rely on heuristics to make judgments. Heuristics can be useful, but they can also lead to biases: for example, the judgment that not being able to see clearly = being far away is correct in most cases, but if you intentionally obscure something, people may mistakenly believe it is farther away.

When judging probabilities and valuations, people often use three heuristics: representativeness, availability, and adjustment and anchoring.

Representativeness Heuristic

1. ==People often confuse representativeness with probability==. A neighbor describes Steve as shy, taciturn, helpful, but uninterested in people and the world. When asked to judge which profession is more likely for him: farmer, salesperson, pilot, librarian, or doctor, people will mistakenly believe Steve is a librarian because it fits their stereotype of that profession.
2. So what factors can help improve probability judgments?
   1. Prior probability, also known as ==base rates==. When considering representativeness, especially when presented with meaningless evidence, people tend to subconsciously ignore base rates and incorrectly use representativeness to make judgments. In the case of Steve, applying prior probability and Bayesian reasoning is necessary to arrive at the correct answer.
   2. Sample size. Sometimes, sample size can better explain issues than the patterns revealed by the sample.
   3. Correct understanding of odds. The gambler's fallacy, for instance, is the incorrect belief that probabilities will self-correct to the mean during a game of chance, while in reality, it is merely self-dilution; the probability of rolling ten "highs" in a row is the same as rolling a "high" and a "low" fifty-fifty.
   4. Predictability. The higher the predictability, the broader the range of estimated values. For example, a brief assessment of a teacher's teaching level should not be sufficient to judge that teacher's performance five years later.
   5. Caution against overconfidence leading to an illusion of validity in one's predictions, caused by confirmation bias, where people tend to seek evidence that supports their views, pay more attention to information that backs their opinions, or interpret information in ways that support their beliefs. Judgments based on several independent events tend to be more accurate than those based on redundant, correlated events.
   6. Regression to the mean. People often overlook this trend, and even when they are aware of it, they can easily misinterpret its causes. For example, in pilot training, instructors find that praising a pilot after a smooth landing leads to relatively worse performance next time; conversely, criticizing after a hard landing leads to relatively better performance next time. Instructors may mistakenly believe that praise and criticism caused the subsequent performance changes, while in reality, it is merely regression to the mean. Thus, people often overestimate the effects of criticism and underestimate the effects of praise.