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Multi-way sensitivity analysis examines the robustness of a model's results by varying two or more inputs simultaneously across their plausible ranges, revealing not only their individual influence but also the interactions between them that one-way analysis cannot detect. By exploring combinations of assumptions, it shows how conclusions hold up when several uncertainties move together, which may matter where parameters are related or where a decision is sensitive to their joint values. The number of combinations grows quickly as more parameters are included, so the analysis is usually focused on a small number of influential or related inputs. It complements one-way analysis and probabilistic sensitivity analysis in giving a fuller picture of decision uncertainty.

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