Know when intuition is unreliable

Expert intuition is valid only when the domain has regular patterns and you’ve had feedback-rich experience in it.

Why it works

Kahneman and Klein (in a joint paper) established two conditions necessary for expert intuition to be trusted: the domain must have sufficient regularity, and the practitioner must have had enough feedback-corrected exposure to internalize those regularities. Stock pickers in random markets and long-range political forecasters both feel expert, but the domain lacks regularity — so the recognition fires on noise, not signal. Calibration requires knowing which category your domain falls in.

How to do it

1. For each domain you make intuitive judgments in, ask: "Does this domain have stable patterns? Do I get timely, accurate feedback?"
2. If the answer to either is no, treat intuitions as hypotheses to be checked, not as directives to act on.
3. Maintain a log of your intuitive predictions and their outcomes in low-regularity domains to detect overconfidence.
4. In high-stakes low-regularity situations, default to statistical base rates rather than pattern recognition.

Evidence

Kahneman and Klein’s synthesis found that valid expert intuition requires domain regularity and extended practice with feedback. Intuitions in irregular, low-feedback domains (stock markets, long-term geopolitical forecasting) perform near chance despite expert confidence. (observational)

Most real domains are intermediate in regularity — neither chess nor pure noise. This makes clean categorization difficult; the framework is a useful lens, not a binary classifier.

Sources

• Kahneman & Klein (2009), "Conditions for intuitive expertise: A failure to disagree," American Psychologist

Common mistake

Trusting a strong gut feeling in a domain where you have logged many hours but received little valid feedback — experience without corrective feedback builds confidence without accuracy.