Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors |
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Authors: | Kash Barker Yacov Y. Haimes |
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Affiliation: | a School of Industrial Engineering, University of Oklahoma, 202 West Boyd Street, Room 124, Norman, OK 73019, USA b Center for Risk Management of Engineering Systems, University of Virginia, 112 Olsson Hall, Charlottesville, VA 22903, USA |
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Abstract: | Risk-based decision making often relies upon expert probability assessments, particularly in the consequences of disruptive events and when such events are extreme or catastrophic in nature. Naturally, such expert-elicited probability distributions can be fraught with errors, as they describe events which occur very infrequently and for which only sparse data exist. This paper presents a quantitative framework, the extreme event uncertainty sensitivity impact method (EE-USIM), for measuring the sensitivity of extreme event consequences to uncertainties in the parameters of the underlying probability distribution. The EE-USIM is demonstrated with the Inoperability input-output model (IIM), a model with which to evaluate the propagation of inoperability throughout an interdependent set of economic and infrastructure sectors. The EE-USIM also makes use of a two-sided power distribution function generated by expert elicitation of extreme event consequences. |
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Keywords: | Uncertainty analysis Extreme events Risk management Inoperability input-output model Multiobjective decision making |
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