Risk and Uncertainty in the Cost Contingency of Transport Projects: Accommodating Bias or Heuristics, or Both?
Bibliographic record
Abstract
Transport projects are regularly subjected to cost misperformance. The contingency set aside to cover any increases in cost due to risk and uncertainty issues is often insufficient. We review approaches that have been used to estimate a cost contingency. We show that some approaches such as reference class forecasting, which underpins the planning fallacy theory, take a biased view to formulate a contingency. Indeed, there is a perception that the risks and uncertainties that form the parts of a cost contingency cannot be accurately assessed using heuristics. The absence of an overarching theory to support the use of heuristics has resulted in them often being downplayed in a project's investment decision-making process. This article fills this void and provides the theoretical backdrop to support the use of heuristics to formulate a cost contingency. We make a clarion call to reconcile the duality of the bias and heuristic approaches, propose a balanced framework for developing a cost contingency, and suggest the use of uplifts to derisk cost estimates is redundant. We hope our advocacy for a balanced approach will stimulate debate and question the legitimacy of uplifts to solely debias cost estimates.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".