Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items

Uncertainty of cost items is an important aspect of complex projects. Cost uncertainty analysis aims to help decision makers to understand and model different factors affecting funding exposure and ultimately estimate the cost of project. The common practice in cost uncertainty analysis includes breaking the project into cost items and probabilistically capturing the uncertainty of each item. Dependencies between these items are important and if not considered properly may influence the accuracy of cost estimation. However these dependencies are seldom examined and there are theoretical and practical obstacles in modeling them. aper proposes a quantitative assessment framework integrating the inference process of Bayesian networks (BN) to the traditional probabilistic risk analysis. BNs provide a framework for presenting causal relationships and enable probabilistic inference among a set of variables. The new approach explicitly quantifies uncertainty in project cost and also provides an appropriate method for modeling complex relationships in a project, such as common causal factors, formal use of expertsʹ judgments, and learning from data to update previous beliefs and probabilities. The capabilities of the proposed approach are explained by a simple example.