Sensitivity analysis measures how a model's output responds to changes in its inputs. If the conclusion (for example, net benefit > 0) holds across the plausible range of every uncertain input, it is robust. If there is a break-even value inside the plausible range of some input, the conclusion is fragile with respect to that input, and the uncertainty about that input is decision-relevant. Ranking inputs by the swing they cause identifies which uncertainties matter most.
Debate arguments also have inputs: estimates of scale, likelihood, compliance and effect size. A robust case is one that still wins if the judge grants the opponent's more pessimistic numbers. Speakers can demonstrate robustness explicitly (“even if only a third of displaced residents return, the policy still protects …”). Opponents should look for the input where the case is fragile and concentrate their rebuttal there. Arguing about inputs that do not change the conclusion wastes time.
Two Proposition arguments in the corporate-partnerships motion. Argument 1: partnerships reduce emissions directly through supply-chain commitments. It works only if companies follow through on a high share of commitments; if follow-through falls below a break-even level, the argument fails. Argument 2: partnerships provide environmental groups with information about corporate operations that improves later advocacy. It depends less on follow-through. If evidence on follow-through is mixed, Argument 2 is the more robust case. In the interactive, move the pessimism slider and see which argument survives.
Grant your opponent a more pessimistic number and see which argument survives.
Robustness depends on the ranges considered plausible, which are themselves contestable. A model can be robust and still omit the factor that matters most. And a conclusion that is robust as a matter of outcomes may still be rejected on grounds of rights or fairness.