Natural Hazards

, Volume 63, Issue 2, pp 659–671 | Cite as

Default risk-based probabilistic decision model for risk management and control

Original Paper


This study describes how risk-based risk control allocation models work. We begin by discussing the economic rationale for allocating risk control in a diversified organization such as an enterprise. For a probability model for risk control decision making under uncertainty and risk, we propose a model involving stochastic total loss amount constraints with respect to various tolerable default levels. Our main objective is to develop a method that will allow shaping of the risk associated with risk control outcomes. The direct and indirect losses caused by simulated disasters can be estimated using an engineering and financial analysis model. Based on this model, we can generate an exceeding probability curve and then calculate how much of the loss can be eliminated or transferred to other entities should funds be allocated to risk control. The optimal natural disaster risk control arrangement with a probabilistic formulation is explained in this paper. Results from the proposed formulations are compared in case studies. The model attempts to apply risk-based budget guidelines to risk reduction measurement within a portfolio-based risk framework.


Risk control Decision model Natural disaster 


  1. Accorsi R, Zio E, Apostolakis GE (1999) Developing utility functions for environmental decision-making. Prog Nucl Energy 34(4):387–411CrossRefGoogle Scholar
  2. Bar-Hillel M (1980) The base rate fallacy in probability judgments. Acta Psychol 44:211–233CrossRefGoogle Scholar
  3. El-Gayar OF, Fritz BD (2010) A web-based multi-perspective decision support system for information security planning. Decis Support Syst 50:43–54CrossRefGoogle Scholar
  4. Lauras M, Marques G, Gourc D (2010) Towards a multi-dimensional project performance measurement system. Decis Support Syst 48:342–353CrossRefGoogle Scholar
  5. Lin JW, Chen CW, Peng CY (2012) Kalman filter decision systems for debris flow hazard assessment. Nat Hazards 60(3):1255–1266CrossRefGoogle Scholar
  6. Power DJ, Sohal AS, Rahman S (2001) Critical success factors in agile natural disaster risk management: an empirical study. Int J Phys Distrib Logist 31(4):247–265CrossRefGoogle Scholar
  7. Prater E, Biehl M, Smith MA (2001) International natural disaster risk control tradeoffs between flexibility and uncertainty. Int J Opera Prod Manag 21(5/6):823–839CrossRefGoogle Scholar
  8. Raviv A (1979) The design of an optimal insurance policy. Am Econ Rev 69:84–96Google Scholar
  9. Singh MD, Shankar R, Narain R, Agarwal A (2003) Knowledge management in engineering industries—an interpretive structural modeling. J Adv Manag Res 1(1):27–39Google Scholar
  10. Tseng CP, Chen CW (2012) Natural disaster management mechanisms for probabilistic earthquake loss. Nat Hazards 60(3):1055–1063CrossRefGoogle Scholar
  11. Waddell D, Sohal AS (1998) Resistance: a constructive tool for change management. Manag Decis 36(8):543–548CrossRefGoogle Scholar
  12. Yusuf YY, Gunasekaran A, Adeleye EO, Sivayoganathan K (2004) Agile natural disaster risk capabilities: determinants of competitive objectives. Eur J Oper Res 159:379–392CrossRefGoogle Scholar
  13. Zhou HJ, Wang JA, Wan JH et al (2010) Resilience to natural hazards: a geographic perspective. Nat Hazards 53(1):21–41CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Chung Shan Institute of Science and Technology, Armaments BureauTaoyuanTaiwan
  2. 2.Institute of Maritime Information and TechnologyNational Kaohsiung Marine UniversityKaohsiung CityTaiwan, ROC
  3. 3.Global Earth Observation and Data Analysis Center (GEODAC)National Cheng Kung UniversityTainan 701Taiwan

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