Identify and analyze cognitive biases including confirmation bias, anchoring, availability heuristic, and sunk cost fallacy in decision-making contexts. Use this skill when the user needs to audit a decision for bias, understand why a team keeps making the same mistakes, design debiasing interventions, or evaluate whether a conclusion is based on evidence or cognitive shortcuts — even if they say 'are we fooling ourselves', 'why do we keep getting this wrong', or 'is this analysis biased'.
Cognitive biases are systematic deviations from rational judgment. They're not random errors — they're predictable patterns that affect everyone, including experts. This skill helps identify which biases are at play in a specific decision context and design countermeasures.
IRON LAW: Name the Specific Bias, Not Just "Bias"
"This decision might be biased" is not analysis. Identify the SPECIFIC
bias by name, explain its mechanism, and show how it applies to this
particular situation. Different biases require different countermeasures.
For the full bias catalog (12 biases × mechanism × example) and
debiasing techniques per bias, see references/debiasing-protocols.md.
# Cognitive Bias Audit: {Decision Context}
## Decision Under Review
- Decision: {what is being decided}
- Decision-makers: {who}
- Current leaning: {which way they're leaning}
## Biases Identified
| Bias | Evidence | Impact | Countermeasure |
|------|----------|--------|---------------|
| {name} | {specific behavior/reasoning} | H/M/L | {debiasing technique} |
## Debiased Recommendation
{What the decision looks like after accounting for identified biases}
Scenario: Company deciding whether to continue a failing product launch (6 months in, NT$5M spent)
| Bias | Evidence | Impact |
|---|---|---|
| Sunk cost | "We've already invested NT$5M, we can't stop now" | High — past spending is irrecoverable and irrelevant to the forward decision |
| Overconfidence | "Our revised forecast shows it will turn around in Q3" — but previous 3 forecasts were also wrong | Medium — team is systematically overestimating success probability |
| Confirmation | Team only citing the 2 positive customer reviews while ignoring 50 negative ones | High — selectively filtering information |
Debiased question: "If a competitor offered us this product line for free, would we take it?" If the answer is no, the product should be discontinued regardless of sunk costs ✓
references/debiasing-protocols.md