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A Survey of Approaches for Assessing and Managing the Risk of Extremes

Author

Listed:
  • Vicki M. Bier
  • Yacov Y. Haimes
  • James H. Lambert
  • Nicholas C. Matalas
  • Rae Zimmerman

Abstract

In this paper, we review methods for assessing and managing the risk of extreme events, where “extreme events” are defined to be rare, severe, and outside the normal range of experience of the system in question. First, we discuss several systematic approaches for identifying possible extreme events. We then discuss some issues related to risk assessment of extreme events, including what type of output is needed (e.g., a single probability vs. a probability distribution), and alternatives to the probabilistic approach. Next, we present a number of probabilistic methods. These include : guidelines for eliciting informative probability distributions from experts; maximum entropy distributions; extreme value theory; other approaches for constructing prior distributions (such as reference or noninformative priors); the use of modeling and decomposition to estimate the probability (or distribution) of interest; and bounding methods. Finally, we briefly discuss several approaches for managing the risk of extreme events, and conclude with recommendations and directions for future research.

Suggested Citation

  • Vicki M. Bier & Yacov Y. Haimes & James H. Lambert & Nicholas C. Matalas & Rae Zimmerman, 1999. "A Survey of Approaches for Assessing and Managing the Risk of Extremes," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 83-94, February.
  • Handle: RePEc:wly:riskan:v:19:y:1999:i:1:p:83-94
    DOI: 10.1111/j.1539-6924.1999.tb00391.x
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    References listed on IDEAS

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    1. Ali Mosleh & Vicki Bier, 1992. "On Decomposition and Aggregation Error in Estimation: Some Basic Principles and Examples," Risk Analysis, John Wiley & Sons, vol. 12(2), pages 203-214, June.
    2. Ali, Mukhtar M, 1977. "Probability and Utility Estimates for Racetrack Bettors," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 803-815, August.
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    8. Qian Zhou & James H. Lambert & Christopher W. Karvetski & Jeffrey M. Keisler & Igor Linkov, 2012. "Flood Protection Diversification to Reduce Probabilities of Extreme Losses," Risk Analysis, John Wiley & Sons, vol. 32(11), pages 1873-1887, November.
    9. James D. Englehardt, 2002. "Scale Invariance of Incident Size Distributions in Response to Sizes of Their Causes," Risk Analysis, John Wiley & Sons, vol. 22(2), pages 369-381, April.
    10. Nima Khakzad & Faisal Khan & Paul Amyotte, 2015. "Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1336-1347, July.
    11. Smith, Curtis L., 2020. "Representing external hazard initiating events using a Bayesian approach and a generalized extreme value model," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    12. Pietro Turati & Nicola Pedroni & Enrico Zio, 2017. "An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 147-159, January.
    13. Seth D. Baum, 2015. "Risk and resilience for unknown, unquantifiable, systemic, and unlikely/catastrophic threats," Environment Systems and Decisions, Springer, vol. 35(2), pages 229-236, June.
    14. Convertino, Matteo & Annis, Antonio & Nardi, Fernando, 2019. "Information-theoretic Portfolio Decision Model for Optimal Flood Management," Earth Arxiv k5aut, Center for Open Science.

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