Info-gap theory models uncertainty formula_1 (the horizon of uncertainty) as nested subsets formula_2 around a point estimate formula_3 of a parameter: with no uncertainty, the estimate is correct, and as uncertainty increases, the subset grows, in general without bound. The subsets quantify uncertainty – the horizon of uncertainty measures the "distance" between an estimate and a possibility – providing an intermediate measure between a single point (the point estimate) and the universe of all possibilities, and giving a measure for sensitivity analysis: how uncertain can an estimate be and a decision (based on this incorrect estimate) still yield an acceptable outcome – what is the margin of error?