Modelling economic project uncertainty: Beware of best practice

Quantitative risk and uncertainty modeling of economic costs and benefits incurred by execution of large projects is usually done by representation of uncertain input variables by probability distributions and subsequent calculation of uncertain output variables by Monte Carlo simulation. This approach is widely recommended in the literature and even by the EU as a guide to handling of large infrastructure investment projects to avoid underestimation of cost and overestimation of benefits. Thus the probabilistic approach is established as a de facto best practice and also a broadly accepted tool among engineers and economists in construction companies and public authorities. At the same time there are an increasing number of reports documenting serious cost overruns and benefit shortfalls as well as ex post results beyond the uncertainty limits predicted by ex ante analysis thereby challenging the relevance and usefulness of the probabilistic approach to modeling of economic project uncertainty. This paper points out some of the weaknesses and pitfalls of the probabilistic modeling approach and explains why it can lead to wrong conclusions due to loss of information during information processing. Special attention is given to loss of focus on extreme economic outcomes occurring and to distortion of prior knowledge of project base case information. Alternatively, it is proposed to represent uncertain variables by intervals and fuzzy numbers and it is demonstrated that this approach has a number of advantages over the probabilistic approach. By using triangular uncertainty distributions the two approaches are compared and a study of a railway line investment case is presented suggesting that a combination of the two approaches have some potential advantages.