A Simple Method to Include Uncertainties in Cost-Benefit Analyses

  • Pierrick NicoletEmail author
  • Michel Jaboyedoff
  • Sébastien Lévy
Conference paper


Cost-benefit analysis is a widely used tool in the field of risk induced by natural hazards in order to (1) compare different mitigation options (2) determine if a protection measure is worth to subsidize and (3) prioritize mitigation measures over a region. These analyses are affected by many uncertainties, although the most uncertain parameter in risk assessment is generally the event frequency. As a result, when cost-benefit analyses are compared over different sites, potentially studied by diverse specialists, with different uncertainties and/or errors on the frequency, an unfortunate decision is likely to result from these analyses, especially as the results looks precise and objective. This study proposes to include an uncertainty on the input parameters, by means of triangular distributions, in order to include the experts uncertainty, as well as the natural variability. Special emphasis is laid on the simplicity of the procedure, since assessing all parameters of the distributions would be time-consuming and difficult. A discussion on the relevance of correlating the random variables used for the sampling process is also presented.


Uncertainty Triangular distribution Cost-benefit 


  1. Bründl M, Romang HE, Bischof N, Rheinberger CM (2009) The risk concept and its application in natural hazard risk management in Switzerland. Nat Hazard Earth Syst 9(3):801–813. doi: 10.5194/nhess-9-801-2009.
  2. Bründl M, Winkler C, Baumann R (2012) “EconoMe-Railway” a new calculation method and tool for comparing the effectiveness and the cost-efficiency of protective measures along railways. In: 12th congress INTERPRAEVENT 2012 - Grenoble/FranceGoogle Scholar
  3. Dorren L, Sandri A, Raetzo H, Arnold P (2009) Landslide risk mapping for the entire Swiss national road network. In: Landslide processes: from geo morphologic mapping to dynamic modelling, CERG, Utrecht University and University of StrasbourgGoogle Scholar
  4. Dussauge-Peisser C, Helmstetter A, Grasso JR, Hantz D, Desvarreux P, Jeannin M, Giraud A (2002) Probabilistic approach to rock fall hazard assessment: potential of historical data analysis. Nat Hazard Earth Syst 2(1/2):15–26. doi: 10.5194/nhess-2-15-2002.
  5. Griffiths RC (1978) On a bivariate triangular distribution. Aust J Stat 20(2):183–185. doi: 10.1111/j.1467-842X.1978.tb01304.x.
  6. Kahneman D, Tversky A (1982) Intuitive prediction: biases and corrective procedures. In: Slovic P, Tversky A, Kahneman D (eds) Judgment under uncertainty: heuristics and biases. Cambridge University Press, Cambridge, pp 414–421CrossRefGoogle Scholar
  7. Kotz S, Van Dorp JR (2004) Beyond beta: other continuous families of distributions with bounded support and applications. World Scientific Publishing, SingaporeGoogle Scholar
  8. Krummenacher B, Dolf F, Gauderon A, Winkler C, Bründl M, Gutwein P, Baumann R, Nigg U (2010) EconoMe 2.0 : Programme de calcul en ligne du caractère économique des mesures de protection contre les dangers naturels. OFEVGoogle Scholar
  9. Loat R, Petrascheck A (1997) Consideration of flood hazards for activities with spatial impact. Recommendations, Federal Office for Water Management, Federal Office for Spatial Planning, Federal Office for the Environment, Forests and Landscape, Biel/Bienne, SwitzerlandGoogle Scholar
  10. Raetzo H, Lateltin O, Bollinger D, Tripet JP (2002) Hazard assessment in Switzerland-codes of practice for mass movements. B Eng Geol Environ 61(3):263–268CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pierrick Nicolet
    • 1
    Email author
  • Michel Jaboyedoff
    • 1
  • Sébastien Lévy
    • 2
  1. 1.University of LausanneLausanneSwitzerland
  2. 2.Direction Générale de L’Environnement, Etat de VaudLausanneSwitzerland

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