Smart heuristics for decision-making in the ‘wild’: Navigating cost uncertainty in the construction of large-scale transport projects

Peter E.D. Love, Lavagnon A. Ika, Jeffrey K. Pinto

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Statistical approaches such as Reference Class Forecasting and Monte Carlo Simulation are widely used to estimate the cost contingency of large-scale transport projects (>$500 million) to mitigate cost overruns during construction. Such approaches may accommodate exposure to risk, but they will fall short in the face of the irreducible uncertainty that confronts project delivery. An underused alternative for formulating a cost contingency is smart heuristics (i.e. simple task-specific decision strategies), which are superior to statistical reasoning under Knightian uncertainty. We set forth an agenda for research on building and using an ‘adaptive toolbox’ of ecologically rational heuristics that decision-makers can apply to produce more accurate contingency estimates for large-scale transport projects. We identify several methodological considerations to support the adaptation and discovery of new heuristics for decision-makers to navigate judgments under uncertainty during the contingency estimation process. The implications for research, policy, and practice are also identified. The contributions of our paper are twofold as we: (1) provide a platform for challenging the effectiveness of the prevailing convention of using statistical reasoning to estimate a project’s cost uncertainty; and (2) identify an avenue for testing existing and discovering new heuristics that can assist decision-making in projects.

Original languageEnglish (US)
JournalProduction Planning and Control
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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