── ── Cognitive bias
Availability Heuristic
The availability heuristic: people estimate probability by how easily examples come to mind. Vivid, recent, and media-reported events are systematically overestimated; routine, statistical events are underestimated. Five triggers: recency, vividness, media coverage, personal experience, imaginability. Structural fix: reference-class forecasting — answer from base-rate data, not memory.
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How it works
Step 1 — Specify the estimate: probability/frequency being estimated · current intuitive estimate · basis (memory/news/data) · decision that depends on it.
Step 2 — Identify the reference class: population of comparable cases · size · time horizon · inclusion/exclusion criteria. (Getting this wrong is the main failure mode.)
Step 3 — Get the base rate: actual frequency in the reference class · source · confidence level.
When to use it
- user says 'we keep hearing about X so it must be common', 'that just happened so it's risky', 'the news is full of stories about this', 'I've seen this a lot lately so it's probable', or is making a risk/frequency estimate driven by memorable examples rather than data
When not to use it
the available evidence is genuinely representative of the reference class; the decision warrants heavy precautionary weight on rare but catastrophic/irreversible risks.
Worked example
Vivid AI Stories vs. Base Rates (2023–2026)
Between the launch of ChatGPT in late 2022 and 2026, public and boardroom estimates of AI's risks and opportunities were shaped less by aggregate data than by a handful of unforgettable stories: a viral demo that looked like magic, a dramatic "AI replaced our whole team" layoff headline, a safety scare that dominated a news cycle. Each is recent, vivid, heavily covered, and easy to picture — all five availability triggers firing at once. The pattern is the skill's core failure mode at population scale…
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
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