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)

• Engine mode: user has a concrete judgment → audit for base rate neglect and conjunction fallacy immediately.
• Coach mode: user is unfamiliar → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line: we judge probability by similarity to a prototype, which feels like reasoning but ignores base rates.
2. Check fit: is a vivid profile driving the judgment? Is a base rate available but being ignored?
3. Elicit their real case: what is the judgment, what profile is driving it, what is the actual base rate?

[WAIT — do not advance until user responds]

4. Run The Process one step at a time: state base rate → apply Bayesian update → decompose any conjunction.

[WAIT — do not advance until user responds]

5. Close by naming the calibrated probability that replaced the prototype-matching estimate.

[WAIT — do not advance until user responds]

The Process

Step 1 — Identify judgment + profile: What probability estimate is being made? What profile information is driving it?

Step 2 — Reference class + base rate: What is the base rate of this outcome in the relevant reference class? Was it stated before the profile assessment? (If N while a profile is present, representativeness is active.)

Step 3 — Conjunction test: Does the judgment involve multiple attributes (X and Y and Z)? P(A AND B) ≤ P(A). If the conjunction was rated more probable than a component, the conjunction fallacy is active.

Step 4 — Bayesian correction: Prior (base rate) × Likelihood ratio → Posterior. Never start from the profile; always start from the base rate.

Step 5 — Prototype audit: Was the prototype built from survivorship-biased examples? What did failures that matched this prototype look like?

Step 6 — Calibrated output:

Base rate: [X%] | Profile adjustment: [direction + magnitude] | Corrected estimate: [Y%]
Conjunction (if applicable): P(A AND B) ≤ [Z%]
Recommendation: proceed / do not proceed / gather more data

→ Method in Action: Tversky & Kahneman 1972 + 1983 — The Linda Problem · The Hot Hand in Basketball

→ 2026 lens: "The Next OpenAI" — Representativeness in AI-Startup Investing (2023–2026)

Pack: Representativeness Across Domains

Domain	Prototype trigger	Base rate ignored	Correction
VC screening	Founder matches "winner" archetype	75–90% of startups fail	State failure base rate before profile
Hiring	Candidate looks like top performers	50–70% of hires underperform	Structured assessment replacing holistic match
Customer persona	"Target customer = power user"	Actual distribution is wide	Segment by behavior data, not persona
Product launch	"This looks like Slack / Airbnb"	Analogous success rate < 10%	State success rate for the category explicitly

Applying It Well

• State the base rate before assessing the profile — never after
• Treat profile as evidence that updates a prior, not as the primary input
• Audit every prototype for survivorship bias: what did the failures look like?
• When a conjunction is involved, decompose it — each added attribute reduces joint probability

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Rationalization	Reality
[D] "He just has the DNA of a founder — I know one when I see one."	Representativeness at maximum activation. What is the base rate for "looks like a winner" actually winning?
[D] "This is exactly the profile of a successful hire."	Profile built from visible successes. How many with this profile failed? Never checked.
[D] "She's a bank teller AND deeply committed to the cause."	Conjunction stated. P(A AND B) ≤ P(A). Narrative fit is not probability.
[D] "Every great company looked like this in the early days."	Survivorship bias generating the prototype. How many that looked like this failed?
[D] "I've seen this pattern before — it always plays out the same way."	How many times did a similar pattern occur and play out differently?
[D] "The more I learn about her, the more I'm convinced."	Added detail increases coherence, not probability. Each condition decreases joint probability.
[D] "I trust my gut — I've been doing this for 20 years."	Accumulates calibration error, not correction, without structured feedback loops.
→ Add [O] entries here after each real use — paste the actual failure pattern	What went wrong and why

Red Flags

• Probability judgment driven by vivid narrative with no explicit base rate
• Conjunction of positive attributes treated as more likely than any single attribute
• Prototype drawn entirely from visible successes; failure base rates never checked
• "Pattern recognition" cited without stating the pattern's historical hit rate

Verification

• Reference class defined and base rate stated before profile assessment
• Conjunction decomposed; no conjunction rated more probable than its least probable component
• Prototype audited for survivorship bias
• Bayesian update applied: prior × likelihood → posterior
• Final estimate anchored to base rate, not profile similarity

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Part of deciqAI Knowledge Skills — 227 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/representativeness-heuristic · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/representativeness-heuristic.json