Judging probability by similarity to stereotypes or prototypes, while ignoring base rates and sample size.
System 1 assesses likelihood by asking 'how similar is this to my mental prototype?' rather than 'what are the actual probabilities?' This produces the famous Linda problem: people judge 'feminist bank teller' as more probable than 'bank teller' because it better matches the description, violating basic probability rules. The heuristic makes us see patterns in randomness, trust small samples as representative of populations, and commit the base rate fallacy by judging individuals by similarity to stereotypes rather than statistical frequency.
Investors see a company with charismatic leadership and innovative products, judge it similar to past success stories like Apple, and overestimate its probability of success while ignoring base rates (most startups fail).
If something matches a pattern or stereotype, it's probably true—similarity to a prototype doesn't determine probability.
In the famous Linda problem, why do people judge 'feminist bank teller' as more probable than 'bank teller'?
An investor sees a startup with charismatic founders, innovative technology, and a compelling vision. How might the representativeness heuristic lead to a poor investment decision?
Logically equivalent choices produce different decisions when framed differently (as gains vs. losses, or with different reference points).
PrincipleContinuing an endeavor because of previously invested resources (time, money, effort) that cannot be recovered, even when continuing is irrational.
PrincipleFast, automatic, unconscious cognitive processing that operates through pattern recognition and associative memory without deliberate effort.
Mental ModelSlow, effortful, conscious cognitive processing required for complex calculations, unfamiliar tasks, and deliberate reasoning.
Mental ModelThe tendency to rely too heavily on the first piece of information encountered (the anchor) when making decisions, even when it's arbitrary or irrelevant.
PrincipleJudging the frequency or probability of events by how easily examples come to mind, leading to overestimation of vivid, recent, or emotional events.
PrincipleWhen faced with a difficult question, System 1 automatically substitutes an easier question without conscious awareness of the switch.
FrameworkLosses hurt approximately twice as much as equivalent gains feel good, making people risk-averse for gains and risk-seeking for losses.
PrincipleJudging probability by similarity to stereotypes or prototypes, while ignoring base rates and sample size.
Investors see a company with charismatic leadership and innovative products, judge it similar to past success stories like Apple, and overestimate its probability of success while ignoring base rates (most startups fail).
If something matches a pattern or stereotype, it's probably true—similarity to a prototype doesn't determine probability.
Representativeness Heuristic is explored in depth in "Thinking, Fast and Slow" by Daniel Kahneman. Distilo provides a deep AI-powered analysis with key insights, audio narration, and practical frameworks.