This paper previews the literature on megaprojects contingency determination techniques. Project cost estimate has two components, the baseline estimate and contingency estimate. Sum of these two estimates forms an initial cost estimate for the project. Contingency cost is added to make provision for uncertainties. Final project cost often exceeds the initial cost estimate. Similarly, final project schedule often exceeds the initial schedule. The deviation of the final cost from the initial cost comes as a result of poor contingency determination. The following techniques are usually used to estimate contingencies: traditional percentage, Monte Carlo (risk analysis), artificial neural networks, regression analysis, expert judgement, and case based reasoning. Regression methods are widely used especially in megaprojects in the energy sector, because of the belief that this technique performs better compared to others. When there is no direct historic data regression methods become useless. To overcome this challenge, system dynamics and Dirichlet process techniques are proposed.
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