| How can a comparison remain useful when future conditions or models are unsettled? | Stress-test declared performance and comparison claims across justified conditions; examine feasible adaptation where it matters. | Optimize one forecast or call an unspecified staged policy robust. | Adapt: :4.1–:4.4 and :4.6 return bounded holding and failure regions. Extra scenario and response analysis is accepted when a single forecast or assumed flexibility can conceal failure. | Lempert et al.’s 2024 DMDU analysis supplies the current robust-decision line; the 2019 DAPP chapter supplies pathway, timing, and failure-condition distinctions. Their applied domains do not supply universal thresholds, scenario probabilities, or local authority. | Reopen when an omitted condition, implementation lead time, or response constraint defeats the stated region. |
| Which sensitivity result should guide further inquiry? | Link local and joint sensitivity to the decision boundary and the value of attainable information. | Use output variance or a one-factor chart as a universal research priority. | Adapt: :4.3–:4.5 distinguish magnitude sensitivity, reversal, and probe value. More computation is justified only when the added question can change the bounded return; actual probe choice remains separate. | Borgonovo et al.’s 2026 review is the synthesis candidate for sensitivity and information acquisition. Its formal approaches need their own model assumptions; C.11 retains the local choice and probe-worthiness result. | Reopen when the feasible probe, decision window, dependency model, or costs change. |
| What can a central-input response conceal? | Compare the central response with a symmetric endpoint mean and test consequential thresholds separately. | Rely on the central response alone or infer a whole-region property from three points. | Adapt: :4.3.1 and :5.1.1 make a finite nonlinear-response check usable without inventing probabilities. | Taleb and West’s 2023 finite-difference and convexity account, §III-C and Appendix B, supplies the distinction between a finite comparison and stronger smoothness, regional or probability claims. Its clinical models do not validate a decision-support response model. | Reopen when the response model, scale, supported domain, subject or decision threshold changes. |
| What if the value model, rather than the forecast, is incomplete? | Test relations across the models compatible with the expressed preferences. | Treat one fitted weight vector as uniquely known. | Adapt: :4.1–:4.4 retain value-dependent reversals instead of calling preference uncertainty factual noise. The deliberate trade-off is a possibly larger retained set. | Greco, Słowiński, and Wallenius’s 2025 MCDA review supplies robust ordinal regression as a best-known-line candidate for this question, not a requirement to use one algorithm or to collapse participants’ values. | Reopen when elicitation or a legitimately governed value decision changes the compatible model set. |