Standard Bayesian epistemology prescribes calibrated credences: an agent who believes proposition P with probability 0.98 should act with exactly 98% confidence, maintaining visible 2% uncertainty at all times. This paper argues that this prescription is not merely impractical — it is actively irrational for human agents, because it confuses the epistemic act of calibration with the behavioral act of commitment. We introduce the **Pragmatic Certainty Theorem**: for human agents making binary real-world decisions, rounding 98% credence to 100% operational certainty (while maintaining second-order awareness of the residual uncertainty) produces superior outcomes compared to maintaining expressed 2% uncertainty throughout execution. The mechanism is threefold: (1) humans cannot neuropsychologically act on fine-grained probability differences near the extremes; (2) expressed uncertainty bleeds into commitment, degrading LCC coupling and Q (the MAT quality factor); and (3) the threshold effect means that sub-threshold uncertainty can collapse an essentially-correct decision into indecision and failure. The correct division of labor: AI tracks fine-grained probabilities (calibration); humans commit from operational certainty (action). These are sequential, not simultaneous operations.
Brandon Charles Emerick (2026) studied this question.