Uncertainty Is Not Actionability

Two questions can carry the same perfectly calibrated error probability yet favor opposite interventions: reflection repairs one, whereas retrieval repairs the other. Uncertainty omits the oriented state–action effect that determines whether an intervention crosses a decision boundary. We prove that this information gap forces tight worst-case regret $\Delta (1-1/K)$ for every action-blind policy over $K$ interventions, even under perfect calibration and randomized choice. Under decision-preserving transformations, we then characterize which effects are identifiable and derive minimal-dimensional coordinates for the resulting maximal invariants: anchored margins for answer scores and anchored cycles for assignment scores. When these coordinates are hidden, black-box logs reveal only the repair, harm, and cost of the executed action. We therefore introduce the Factorized Action-Value Estimator (FAVE), which estimates conditional action value by sharing need, response, and direction across actions. Across 30,918 natural matching cycles, conditional repair rises from 16.8% in the positive-but-insufficient region to 72.7% after boundary crossing. On a 2,000-item, five-intervention portfolio, FAVE exceeds equal-information pooled prediction by .0096 (two-way 95% CI $[.0030,.0163]$) and by .0203 on held-out intervention types; its advantage persists across all predeclared token prices. At an eightfold harm price, validation-selected pooling attains .0768 utility while every fixed intervention has nonpositive population value. Uncertainty locates the incumbent. Actionability predicts where each intervention will move it and what that movement is worth.