cognitive-biases

# Cognitive Biases

Human reasoning relies on heuristics — fast, frugal shortcuts that work well most of the time but produce systematic errors under predictable conditions. Cognitive biases are those systematic errors. They are not random noise; they are patterned departures from normative reasoning that can be anticipated, diagnosed, and partially corrected. This skill catalogs the twelve most consequential biases, each with a mechanism, a diagnostic signal, and a mitigation strategy.

**Agent affinity:** tversky (heuristics and biases tradition), kahneman-ct (System 1 / System 2 framing), paul (integration with elements of reasoning)

**Concept IDs:** crit-confirmation-bias, crit-availability-anchoring, crit-intellectual-humility, crit-calibrated-confidence

## The Bias Catalog at a Glance

| # | Bias | Mechanism | Diagnostic signal |
|---|---|---|---|
| 1 | Confirmation bias | Seek and weight supporting evidence more than disconfirming | "I knew it" for every matching case; disconfirming cases feel like "exceptions" |
| 2 | Availability heuristic | Judge probability by how easily examples come to mind | Vivid recent events dominate risk estimates |
| 3 | Anchoring | First number or idea biases subsequent estimates | Second guess is close to the first even with new information |
| 4 | Representativeness | Judge by resemblance to a stereotype, ignoring base rates | Ignoring how rare the category actually is |
| 5 | Framing effects | Same content, different phrasing, different choices | Preferences flip when the same option is described as gain vs. loss |
| 6 | Hindsight bias | Past events feel inevitable after the fact | "It was obvious" retrospectively; no one predicted it |
| 7 | Overconfidence | Confidence intervals are too narrow relative to accuracy | 90% confidence intervals contain the truth ~50% of the time |
| 8 | Motivated reasoning | Conclusion drives evidence evaluation, not vice versa | Evidence quality is judged leniently for favored conclusions |
| 9 | Sunk cost fallacy | Past investment justifies continued investment | Continuing a failing project because "we've already spent so much" |
| 10 | Fundamental attribution error | Others' behavior attributed to character; one's own to circumstance | "They're incompetent" vs. "I was having a bad day" |
| 11 | In-group favoritism | Judgments tilt toward one's own group | Same behavior is praised in allies, criticized in opponents |
| 12 | Base rate neglect | Ignoring prior probabilities in favor of new information | Updating too strongly on a single diagnostic result |

## Bias 1 — Confirmation Bias

**Mechanism.** Once a hypothesis is entertained, the mind preferentially searches for, attends to, remembers, and weighs evidence that supports it. Disconfirming evidence is overlooked, dismissed, or explained away.

**Worked example.** A researcher convinced that a particular herb cures headaches runs a trial. When the trial shows no effect, she attributes this to "impure samples." When a later trial shows a small effect, she counts this as confirmation. Over twenty trials, the balance is null — but her belief strengthens throughout.

**Mitigation — the disconfirmation pass.** Before committing to a conclusion, list three observations that would falsify it, then actively search for each. If you cannot find a way the claim could be wrong, you do not understand the claim.

**Mitigation — the pre-mortem.** Imagine the project has failed. What is the most likely reason? This reframes confirmation into disconfirmation without feeling threatening.

## Bias 2 — Availability Heuristic

**Mechanism.** Probability is estimated by the ease with which instances come to mind. Vivid, recent, or emotionally charged events are over-weighted; common but unremarkable events are under-weighted.

**Worked example.** After a high-profile plane crash, travelers estimate air travel as more dangerous than driving, even though per-mile statistics show driving is approximately 60 times more dangerous. The crash is vivid; driving fatalities are individually unmemorable.

**Mitigation — consult base rates.** Before judging probability from memory, check whether actual frequency data exists. The memory is a sample from attention, not from reality.

**Mitigation — reverse the question.** Instead of "how risky is X?" ask "how many times did X happen last year per million attempts?"

## Bias 3 — Anchoring

**Mechanism.** Numerical estimates are biased toward any anchor value available at the moment of estimation, even when the anchor is arbitrary or irrelevant.

**Worked example.** A classic study asked participants to write down the last two digits of their social security number, then estimate the price of a bottle of wine. People with higher digits consistently gave higher estimates. The digits had zero informational value, yet they shifted estimates by 50-100%.

**Mitigation — multiple independent estimates.** Generate the estimate twice, using different starting points, then average.

**Mitigation — consider the opposite.** Explicitly ask "why might my estimate be too high?" and "why might it be too low?" before committing.

## Bias 4 — Representativeness Heuristic

**Mechanism.** Probability is judged by how much an instance resembles a stereotype of the category, rather than by the base rate of the category.

**Worked example (Tversky & Kahneman, 1983).** Linda is 31, single, outspoken, and deeply concerned with issues of discrimination and social justice. Which is more probable?
1. Linda is a bank teller.
2. Linda is a bank teller and active in the feminist movement.

Most people pick (2), but (2) is a conjunction of (1) with another claim, so P(2) <= P(1) by the conjunction rule. Representativeness overrode probability.

**Mitigation — apply the conjunction rule.** A more specific claim is always less probable than its components.

**Mitigation — consult base rates.** How many bank tellers are there? How many active feminists? The base rates are what ac