every linear program you have ever written down was a lie: the coefficients came from measurements, forecasts and vendor spreadsheets, and the optimal vertex — sitting, by design, on the boundary of the feasible region — shatters the moment any of them wobbles. 𐃏 robust optimisation is the pessimist’s response: declare a set \(\mathcal{U}\) of realisations you refuse to be hurt by, and demand feasibility for every member of it. no distributions, no expectations, no scenarios — just a set and a worst case. the surprise, and the reason the field exists, is that this worst case can usually be folded back into a deterministic problem of the same (or nearly the same) complexity class.1