Principlev1

When identifying overconfidence in retrospective prediction

When identifying overconfidence in retrospective prediction reviews, widen future confidence intervals by 2-3x in that domain until hit rates align with stated probabilities, using mechanical correction where intuitive adjustment fails.

Why This Is a Principle

This principle derives from Systematic Overconfidence Taxonomy (systematic overprecision), Hindsight Bias and Calibration Necessity (calibration requires external feedback), and Bias Blind Spot Asymmetry (can't see own biases). It prescribes a mechanical correction factor because awareness alone doesn't fix calibration—the feeling of 'too wide' is itself miscalibrated.

Source Lessons

L-0143

Overconfidence is the default calibration error

Your brain does not fail randomly. It fails in a specific, measurable, predictable direction: too much confidence. Across decades of research, in every population tested, the dominant calibration error is overconfidence — believing you know more than you do, that your estimates are more precise than they are, and that your performance exceeds what it actually achieves.

Connections

Derived From (3)

AxiomHindsight Bias and Calibration Necessity AxiomSystematic Overconfidence Taxonomy AxiomBias Blind Spot Asymmetry

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