Skip to main content
Log in

A new viewpoint on risk control decision models for natural disasters

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

This study describes a critical assessment of the risk control decision model from a methodological perspective and identifies major shortcomings with the employment of enhanced formal evaluation and decision-making methods. This in turn could have major applications for natural disaster risk control. The methodology follows the description of interpretive structural modeling (ISM), which is an interactive learning process in which a set of different and directly related elements is structured to form a comprehensive systemic model. The next step explores the potentials of different mathematical programming approaches in order to improve decision making, i.e., for the development of an economic utility constrained-maximization model that addresses the issue of optimal budget allocation under a trade-off framework. Several aspects of risk and uncertainty are discussed within the context of an economic utility constrained-maximization model with a major focus on the importance of risk and uncertainty in research evaluation, and how the strategy determines the insurance and risk control plans.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Agarwal, Ashish, Shankar R, Tiwari M (2007) Modeling agility of supply chain. Ind Mark Manag 36(4):443–457

    Google Scholar 

  • Bar-Hillel M (1980) The base rate fallacy in probability judgments. Acta Psychol 44:211–233

    Article  Google Scholar 

  • Bolanos R, Fontela E, Nenclares A, Paster P (2005) Using interpretive structural modeling in strategic decision making groups. Manag Decis 43(6):877–895

    Google Scholar 

  • Handfield RB, Bechtel C (2002) The role of trust and relationship structure in improving supply chain responsiveness. Ind Mark Manag 31:367–382

    Google Scholar 

  • Haque CE, Etkin D (2007) People and community as constituent parts of hazards: the significance of societal dimensions in hazards analysis. Nat Hazards 41(2):271–282

    Article  Google Scholar 

  • Jharkharia S, Shankar R (2005) IT-enablement of supply chains: understanding the barriers. J Enterp Inf Manag 18(1):11–27

    Google Scholar 

  • Katayama H, Bennett D (1999) Agility, adaptability and leanness: a comparison of concepts and a study of practice. Int J Prod Econ 60–61:43–51

    Article  Google Scholar 

  • Mandal A, Deshmukh SG (1994) Vendor selection using interpretive structural modeling (ISM). Int J Oper Prod Manag 14(6):52–59

    Google Scholar 

  • 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):247–265

    Article  Google Scholar 

  • Prater E, Biehl M, Smith MA (2001) International natural disaster risk control tradeoffs between flexibility and uncertainty. Int J Oper Prod Manag 21(5/6):823–839

    Article  Google Scholar 

  • Raaijmakers R, Krywkow J, van der Veen A (2008) Flood risk perceptions and spatial multi-criteria analysis: an exploratory research for hazard mitigation. Nat Hazards 46(3):307–322

    Article  Google Scholar 

  • Ravi V, Shankar R (2005) Analysis of interactions among the barriers of reverse logistics. Technol Forecast Soc Change 72:1011–1029

    Google Scholar 

  • Sage AP (1977) Interpretive structural modeling: methodology for largescale systems. McGraw-Hill, New York

    Google Scholar 

  • Saxena JP, Sushil, Vrat P (1990) The impact of indirect relationships in classification of variables—a MICMAC analysis for energy conservation. Syst Res 7(4):245–253

    Article  Google Scholar 

  • Sharma HD, Gupta AD, Sushil (1995) The objectives of waste management in India: a future inquiry. Technol Forecast Soc Change 48:285–309

  • Singh MD, Shankar R, Narain R, Agarwal A (2003) Knowledge management in engineering industries—an interpretive structural modeling. J Adv Manag Res 1(1):27–39

    Google Scholar 

  • Waddell D, Sohal AS (1998) Resistance: a constructive tool for change management. Manag Decis 36(8):543–548

    Article  Google Scholar 

  • Warfield JW (1974) Developing interconnected matrices in structural modeling. IEEE Transcr Syst Men Cybern 4(1): 51–81

    Google Scholar 

  • Yi CS, Lee JH, Shim MP (2010) GIS-based distributed technique for assessing economic loss from flood damage: pre-feasibility study for the anyang stream basin in Korea. Nat Hazards 55(2):251–272

    Article  Google Scholar 

  • Yusuf YY, Gunasekaran A, Adeleye EO, Sivayoganathan K (2004) Agile natural disaster risk capabilities: determinants of competitive objectives. Eur J Oper Res 159:379–392

    Article  Google Scholar 

  • Zhou HJ, Wang JA, Wan JH et al (2010) Resilience to natural hazards: a geographic perspective. Nat Hazards 53(1):21–41

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cheng-Wu Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tseng, CP., Chen, CW. & Tu, YP. A new viewpoint on risk control decision models for natural disasters. Nat Hazards 59, 1715–1733 (2011). https://doi.org/10.1007/s11069-011-9861-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-011-9861-1

Keywords

Navigation