| When should uncertainty be probabilistic, and when should alternatives remain conditional? | Use qualified probabilistic analysis where its premises hold; retain multiple plausible conditions when decision-relevant probabilities or models are unsettled. | Predict-then-act under one agreed forecast, or reject all probabilistic reasoning whenever uncertainty is difficult. | Adapt: :4.1–:4.4 select the representation by the actual basis and test shared failure. Additional scenario work is accepted only when one forecast could conceal a material reversal. | Lempert et al.’s 2024 DMDU/IPCC analysis is the synthesis candidate for decision relevance despite low-confidence knowledge. It does not make scenarios equiprobable or provide a universal uncertainty taxonomy or local risk threshold. | Reopen when a qualified distribution becomes available, model disagreement changes, or an omitted future defeats the claimed bounds. |
| Which missing evidence deserves a question? | Connect elicitation, sensitivity, and information acquisition to the alternatives and consequences they can change. | Rank unknowns by variance or confidence alone. | Adapt: :4.5–:4.6 require a discriminating question before further inquiry. The practical trade-off is less general data collection for a more explicit decision boundary. | Borgonovo et al.’s 2026 review supplies the current decision-analysis connection among uncertainty, sensitivity, and information value. It is a synthesis, not validation of PSDA’s particular evidence or a universal probe rule. A.10 supplies only bounded reliance; C.11 retains actual local probe choice. | Reopen if another unknown can change eligibility, a model revision changes the discriminator, or no feasible inquiry fits the decision window. |