deciqAI Availability Heuristic
Overview
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.
Composes with bayesian-reasoning, probabilistic-thinking, anchoring, survivorship-bias, framing-effect.
When to Use
- Someone estimates probability from memorable examples rather than data
- Risk perception driven by recent news, vivid anecdotes, or media coverage
- Team is preparing for the last disaster rather than the most likely next one
- Investment or resource-allocation decision follows a recent dramatic event
- Medical, legal, or technical decision made by anecdote rather than reference class
- AI capex, AI valuations, or AI-adoption/displacement estimates are driven by a viral demo, a dramatic layoff headline, or a safety scare rather than deployment and labor base rates
Not when: available evidence is a representative sample; decision warrants precautionary weight on rare catastrophic risks; no reference-class data exists.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a specific risk or frequency estimate → run The Process directly.
- Coach mode: user is new → 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.
- One-line: before trusting an intuitive probability, ask whether it is based on retrieval ease or on a reference class with actual data.
- Check fit: if recent vivid evidence is also representative, use it; otherwise discount and seek base rates.
- Elicit their real case — what probability is being claimed, on what basis?
[WAIT — do not advance until user responds]
- Run The Process one step at a time: reference class → base rate → distortion diagnosis → compare to intuitive estimate.
[WAIT — do not advance until user responds]
- Close: restate corrected estimate as base-rate + specific adjustment, with the adjustment justified.
[WAIT — do not advance until user responds]
The Process
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.
Step 4 — Diagnose availability distortions: recency (is the recent rate representative?) · vividness (dramatic vs. routine?) · media coverage (over-reported?) · personal experience (inflated?) · imaginability (easy to picture → feels more probable?).
Step 5 — Compare: intuitive estimate vs. base rate. If ratio >2×, availability is doing significant work. Adjust toward base rate; justify any residual deviation.
Step 6 — Document: corrected probability · reasoning (base rate × adjustments) · decision implications · calibration log entry.
Output Template
Availability Correction: <event>
Original estimate: <value> | Basis: | Decision:
Reference class: <population, size, time horizon>
Base rate: <frequency> | Source: | Confidence:
Distortions: recency / vividness / media / personal / imaginability
Corrected estimate: <value> | Adjustment rationale:
Decision implications: | Calibration log:
→ Method in Action: Tversky and Kahneman's 1973 Availability Studies · Post-9/11 Flight Avoidance and Road Fatalities
→ 2026 lens: Vivid AI Stories vs. Base Rates (2023–2026)
Pack: Availability Bias Patterns
| Domain | Distortion | Correction |
|---|---|---|
| Personal risk | Overestimate terrorism/plane crashes; underestimate heart disease/car crashes | CDC/BLS mortality statistics |
| Investment | Recent winners feel certain to continue | Long-horizon data; mean reversion priors |
| Hiring | Memorable candidate beats better-qualified one | Structured rubric; reference-class hire data |
| Project planning | Optimism from vivid current plan vs. project-class history | Flyvbjerg reference-class forecasting |
| Medical diagnosis | Recently-seen diagnosis over-weighted | Base-rate-weighted differential diagnosis |
| Strategic planning | Recent competitor event dominates outlook | Multi-year base rates; explicit reference class |
Applying It Well
- Define the reference class before looking up the base rate. Calibration improves with logged predictions vs. outcomes, not exhortation. Present absolute frequencies (deaths per 100,000), not relative risk.
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "But that just happened!" | Recency aids recall; it doesn't increase future probability. Check the longer reference class. |
| [D] "I've seen this many times" | Easy retrieval ≠ high population frequency. |
| [D] "The news is full of stories about this" | Coverage tracks vividness, not frequency. |
| [D] "I'd remember if it were rare" | You remember dramatic-rare more than statistical-common. |
| [D] "I have personal experience" | Personal examples are a tiny, often unrepresentative sample. |
| [D] "The risk is too vivid to ignore" | Vividness ≠ probability. Compute actual probability; respond proportionally. |
| [D] "We have to prepare for the worst" | Sometimes valid. Often over-preparation for spectacular-low-probability events. |
| [D] "The base rate is irrelevant — this case is different" | Be specific about which factors actually move the estimate. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Risk estimate made without consulting actual data
- Single-case vivid examples or recent news are the dominant evidence
- Coverage volume treated as evidence of frequency
- Reference class not explicitly identified
Verification
- Intuitive estimate stated with confidence level
- Reference class identified
- Base rate retrieved from a defensible source
- Availability distortions (recency, vividness, media, personal experience) considered
- Corrected estimate documented with reasoning
- Decision implications computed
- Estimate logged for later calibration
Part of deciqAI Knowledge Skills — 237 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/s/availability-heuristic · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/availability-heuristic.json