Limitations
In applying info-gap theory, one must remain aware of certain limitations.
Firstly, info-gap makes assumptions, namely on universe in question, and the degree of uncertainty – the info-gap model is a model of degrees of uncertainty or similarity of various assumptions, within a given universe. Info-gap does not make probability assumptions within this universe – it is non-probabilistic – but does quantify a notion of "distance from the estimate". In brief, info-gap makes fewer assumptions than a probabilistic method, but does make some assumptions.
Further, unforeseen events (those not in the universe ) are not incorporated: info-gap addresses modeled uncertainty, not unexpected uncertainty, as in black swan theory, particularly the ludic fallacy. This is not a problem when the possible events by definition fall in a given universe, but in real world applications, significant events may be "outside model". For instance, a simple model of daily stock market returns – which by definition fall in the range – may include extreme moves such as Black Monday (1987) but might not model the market breakdowns following the September 11 attacks: it considers the "known unknowns", not the "unknown unknowns". This is a general criticism of much decision theory, and is by no means specific to info-gap, but nor is info-gap immune to it.
Secondly, there is no natural scale: is uncertainty of small or large? Different models of uncertainty give different scales, and require judgment and understanding of the domain and the model of uncertainty. Similarly, measuring differences between outcomes requires judgment and understanding of the domain.
Thirdly, if the universe under consideration is larger than a significant horizon of uncertainty, and outcomes for these distant points is significantly different from points near the estimate, then conclusions of robustness or opportuneness analyses will generally be: "one must be very confident of one's assumptions, else outcomes may be expected to vary significantly from projections" – a cautionary conclusion.
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