Representativeness Heuristic

Overview

The representativeness heuristic is judging probability by how closely something resembles a prototype — overriding actual base rates. Named by Tversky & Kahneman (1972); produces three systematic errors: base rate neglect, the conjunction fallacy (A-and-B feels more likely than A), and insensitivity to sample size.

Composes with bayesian-reasoning (restores the prior), survivorship-bias (failures are invisible in the prototype), confirmation-bias, and anchoring.

When to Use

• Evaluating candidates, screening investments, or persona-based product decisions
• Any probability judgment where a vivid profile or narrative is present
• When "she/he/it looks like X" drives a decision without a stated base rate
• Auditing for the conjunction fallacy (more specific = seemingly more likely)
• Judging AI startups/valuations/capex by resemblance to a prototype ("the next OpenAI/Stripe," AI capex "obviously pays off," AI adoption curves read as durable revenue)

Not when: judgment is purely quantitative; base rate and profile align and Bayesian updating has been done explicitly.

Coaching Novices (Adaptive Front Door)