Smart heuristics for decision-making in the ‘wild’: Navigating cost uncertainty in the construction of large-scale transport projects
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".