MENTAL MODEL · M7555

Confidence-Weighted Assessment

Learn & MetacognitionHigh supportCognitive Science

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Updated 2026-08-22

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INTRODUCTION

English translation pending.

CORE DEFINITION

Confidence-weighted assessment asks for two outputs at once: the judgment itself and the confidence in it. Calibration testing then asks whether judgments held with 80 percent confidence are correct 80 percent of the time, exposing systematic overconfidence or underconfidence. It trains the accuracy of metacognition, revealing the gap between what a person knows and what they merely assume they know.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- Answer twice: state the conclusion, then state the probability you assign to it - Draw the curve: accumulate actual outcomes until claimed confidence can be compared with hit rate - Shave the overconfidence: lower the probabilities of someone whose 80 percent claims hit 60 percent

anchor

Anchor fast decisions

Pairing every judgment with a probability turns a right-or-wrong tally into a calibration curve, which reveals the systematic gap between claimed certainty and observed hit rate. Well-calibrated people predict more accurately over time, because naming a probability forces honesty about what they do not know instead of treating a feeling of certainty as evidence.

MINIMUM ACTION

In progress 0/1

Practice this model in one real situation:

Attach a confidence probability to every judgment you give 2. Record whether each prediction was right until a calibration curve forms 3. If 80 percent confidence hits only 60 percent, mark yourself overconfident and lower the numbers 4. Track calibration quality with a scoring rule such as the Brier score

Check to track your progress (stored locally)

Learning progress0%

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Source support: Explicit

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en.wikipedia.orghttps://en.wikipedia.org/wiki/SuperforecastingZH · Explicit
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