Representation of Risk Scenarios via Euler Diagrams

  • James H. Lambert
  • Priya Sarda
Conference paper


A risk analysis begins with the identification and classification of initiating events. We address the application of Euler Diagrams to guide database queries and to determine when an evolving set of initiating events effectively spans the systems and situation of interest.


Risk Analysis Quality Function Deployment Fault Tree Scenario Identification Risk Scenario 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kaplan S. and Garrick B.J. On the quantitative definition of risk, Risk Analysis, 1981; 1(1): 10–27 Sharit J. A modeling framework for exposing risks in complex systems, Risk Analysis, 2000; 20(4)CrossRefGoogle Scholar
  2. 2.
    Lambert J., Haimes Y., Li D., Schooff R., and Tulsiani V. Identification, ranking, and management of risks in a major system acquisition, J. of Reliability Engineering and System Safety, 2001; 72(3): 315–325CrossRefGoogle Scholar
  3. 3.
    Haimes Y., Matalas N., Lambert J., Jackson B. and Fellows J. Reducing vulnerability of water supply systems to attack, J. of Infrastructure Systems, 1998; 4(4): 164–177CrossRefGoogle Scholar
  4. 4.
    Kaplan S., Haimes Y.Y., and Garrick B. J. Fitting hierarchical holographic modeling into the theory of scenario structuring and a resulting refinement to the quantitative definition of risk, Risk Analysis, 2001; 21(5): 807–819CrossRefGoogle Scholar
  5. 5.
    Wei, B. A unified approach to failure mode, effects and criticality analysis, Proceedings of the Annual Reliability and Maintainability Symposium, IEEE Reliability Society, 1991Google Scholar
  6. 6.
    Kaplan, S. Finding failures before they find us: An introduction to the theory of scenario structuring and the method of anticipatory failure determination, Proceedings of the Ninth Symposium on Quality Function Deployment, 1997; Ann Arbor, MI, QFD InstituteGoogle Scholar
  7. 7.
    Kuzminski, P., Eisele J.S., Garber N., Schwing R., Haimes Y.Y., Li D., Chowdhury M. Improvement of highway safety I: Identification of causal factors through fault-tree modeling, J. of Risk Analysis, 1995; 15(3): 293–312CrossRefGoogle Scholar
  8. 8.
    Basilio R.R., Plourde K.S., Lam T. A systematic risk management approach employed on the CloudSat project, Aerospace Conference, IEEE Proceedings, 2001; Vol. 1: 469–479Google Scholar
  9. 9.
    Lambert J., Patterson C. Prioritization of schedule dependencies in hurricane recovery of transportation agency, J. of Infrastructure Systems, 2002; 8(3): 103–111CrossRefGoogle Scholar
  10. 10.
    Scheringer M., Vogl T., Grote J., Capaul B., Schubert R., Hungerbuhler K. Scenario based risk assessment of multi-use chemicals: Application to Solvents, J. of Risk Analysis, 2001; 21(3): 481–486CrossRefGoogle Scholar
  11. 11.
    Harel D. On Visual Formalisms, Communications of the ACM, 1998; 31(5): 514–530CrossRefMathSciNetGoogle Scholar
  12. 12.
    Halpin T. Information modeling and relational databases, Morgan Kaufmann, New York, 2001Google Scholar
  13. 13.
    Schmidt C.F. Euler diagrams and quantified expressions,∼cfs/305_html/Deduction?EulerDiags.html, 2003Google Scholar
  14. 14.
    Haimes, Y., B. Horowitz, J. Lambert, and J. Monahan. Risk-Based Methodological Framework for Scenario Tracking and Intelligence Collection and Analysis for Terrorism. Research effort sponsored by the National Science Foundation, 2003 through 2006.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • James H. Lambert
    • 1
  • Priya Sarda
    • 1
  1. 1.Center for Risk Management of Engineering Systems and Department of Systems and Information EngineeringUniversity of VirginiaCharlottesvilleUSA

Personalised recommendations