Criticism
A general criticism of non-probabilistic decision rules, discussed in detail at decision theory: alternatives to probability theory, is that optimal decision rules (formally, admissible decision rules) can always be derived by probabilistic methods, with a suitable utility function and prior distribution (this is the statement of the complete class theorems), and thus that non-probabilistic methods such as info-gap are unnecessary and do not yield new or better decision rules.
A more general criticism of decision making under uncertainty is the impact of outsized, unexpected events, ones that are not captured by the model. This is discussed particularly in black swan theory, and info-gap, used in isolation, is vulnerable to this, as are a fortiori all decision theory that uses a fixed universe of possibilities, notably probabilistic ones.
In criticism specific to info-gap, Sniedovich raises two objections to info-gap decision theory, one substantive, one scholarly:
- 1. the info-gap uncertainty model is flawed and oversold
- Info-gap models uncertainty via a nested family of subsets around a point estimate, and is touted as applicable under situations of "severe uncertainty". Sniedovich argues that under severe uncertainty, one should not start from a point estimate, which is assumed to be seriously flawed: instead the set one should consider is the universe of possibilities, not subsets thereof. Stated alternatively, under severe uncertainty, one should use global decision theory (consider the entire region of uncertainty), not local decision theory (starting with a point estimate and considering deviations from it).
- 2. info-gap is maximin
- Ben-Haim (2006, p.xii) claims that info-gap is "radically different from all current theories of decision under uncertainty," while Sniedovich argues that info-gap's robustness analysis is precisely maximin analysis of the horizon of uncertainty. By contrast, Ben-Haim states (Ben-Haim 1999, pp. 271–2) that "robust reliability is emphatically not a worst-case analysis". Note that Ben-Haim compares info-gap to minimax, while Sniedovich considers it a case of maximin.
Sniedovich has challenged the validity of info-gap theory for making decisions under severe uncertainty. He questions the effectiveness of info-gap theory in situations where the best estimate is a poor indication of the true value of . Sniedovich notes that the info-gap robustness function is "local" to the region around, where is likely to be substantially in error. He concludes that therefore the info-gap robustness function is an unreliable assessment of immunity to error.
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