PSD.12:1 - Problem frame

Use this when a favored alternative depends on a forecast, weight, model, or threshold that might change, or when a precise ranking hides plausible reversals. Also use it when the recipient asks whether further information would improve the decision rather than merely improve the estimate.

Start with one claim: what exactly should remain true, under which changes? Vary the decision-bearing conditions within a justified range and inspect where the claim survives or fails. The gain is a bounded statement of stability and a useful next question, not the adjective “robust” applied to an entire project.

The governed object is that robustness account. Sensitivity describes how a result changes with inputs, assumptions, or models. Robustness says whether a declared performance, admissibility, or comparison condition continues to hold across specified variation. A sensitive magnitude can leave the preferred set unchanged; an insensitive average can hide a decisive threshold crossing. Section 4.3.1 supplies a small paired-response test when the response at a central input can conceal such a difference.

Do not use this pattern when a current robustness result already covers the exact comparison and changes now in question. Use direct model validation for whether a model is adequate in the first place, and the authorized choice owner for the actual decision or probe. Stability inside a model does not validate the model or authorize action.