Implications of Info-Gap Uncertainty

Ben‐Haim, Yakov

doi:10.1016/b978-012373552-2/50014-x

Cited by 25 publications

(48 citation statements)

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“…[5] Info gap theory [Ben-Haim, 2006] provides a quantified theory of robustness. It also examines opportuneness, the propitious side to uncertainty, by helping to identify options that may be prone to desirable outcomes under uncertain conditions.…”

Section: Info Gap Decision Theorymentioning

confidence: 99%

“…Since the robustness function expresses immunity against failure ''bigger is better'' and conversely, when considering the opportunity function ''big is bad '' [Ben-Haim, 2006]. Typically values for windfall reward will be far greater than those for critical reward.…”

Section: Info Gap Decision Theorymentioning

confidence: 99%

“…Typically values for windfall reward will be far greater than those for critical reward. These two values reflect the requirements of the decision maker [Ben-Haim, 2006]. So as to inform their judgments, the results of the info gap analysis are presented as a function of r c and r w .…”

Section: Info Gap Decision Theorymentioning

confidence: 99%

“…[14] Info gap analysis has found applications in disciplines as diverse as economics, antiterrorism and environmental protection [Ben-Haim, 2006], all of which are subject to severe uncertainties. For reasons mentioned at the outset of this paper, flood risk management decisions can also be subject to severe uncertainties, which is why info gap theory is particularly appealing.…”

Section: Info Gap Decision Theorymentioning

confidence: 99%

“…[4] This paper introduces a quantified theory of robust decision making, known as information gap theory [Ben-Haim, 2006], or info gap for short, to flood risk management decisions. Info gap has been previously applied to water resource management decisions [Hipel and Ben-Haim, 1999], but to the authors' knowledge, the work described in this paper is the first application to flood risk management decisions.…”

Section: Introductionmentioning

confidence: 99%

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Information gap analysis of flood model uncertainties and regional frequency analysis

Hine

Hall

2010

Water Resources Research

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[1] Flood risk analysis is subject to often severe uncertainties, which can potentially undermine flood management decisions. This paper explores the use of information gap theory to analyze the sensitivity of flood management decisions to uncertainties in flood inundation models and flood frequency analysis. Information gap is a quantified nonprobabilistic theory of robustness. To analyze uncertainties in flood modeling, an energy-bounded information gap model is established and applied first to a simplified uniform channel and then to a more realistic 2-D flood model. Information gap theory is then applied to the estimation of flood discharges using regional frequency analysis. The use of an information gap model is motivated by the notion that hydrologically similar sites are clustered in the space of their L moments. The information gap model is constructed around a parametric statistical flood frequency analysis, resulting in a hybrid model of uncertainty in which natural variability is handled statistically while epistemic uncertainties are represented in the information gap model. The analysis is demonstrated for sites in the Trent catchment, United Kingdom. The analysis is extended to address ungauged catchments, which, because of the attendant uncertainties in flood frequency analysis, are particularly appropriate for information gap analysis. Finally, the information gap model of flood frequency is combined with the treatment of hydraulic model uncertainties in an example of how both sources of uncertainty can be accounted for using information gap theory in a flood risk management decision.

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Section: Info Gap Decision Theorymentioning

confidence: 99%

Section: Info Gap Decision Theorymentioning

confidence: 99%

Section: Info Gap Decision Theorymentioning

confidence: 99%

Section: Info Gap Decision Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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