Breaking the Sisyphean loop: reconceptualizing the treatment of risk and uncertainty in transport projects
Bibliographic record
Abstract
Worldwide transport projects repeatedly exceed their budgets.This problem has received widespread attention over several decades, with little progress made to improve project cost performance.Consequently, this has led to the emergence of the following recurring question: How can public agencies responsible for delivering transport projects improve the estimation accuracy of their actual construction costs?We use the metaphor of the Sisyphean task to assist us in suggesting that the conflation of risk and uncertainty results in an inability to estimate actual construction costs accurately.Ambiguity aversion, an illusion of certainty, and the repeated occurrence of cost deviations, as demonstrated by our illustrative example and analysis of the elemental cost models of 39 transport projects, collectively create a Sisyphean loop that is impossible to break.Thus, we question the prevailing conceptualisation and treatment of risk and uncertainty used to estimate the actual construction costs of projects.We offer a new theoretical lens framed around ecologically rational heuristics and provide suggestions to reconceptualize and recalibrate the treatment and assessment of uncertainty in cost estimates.We hope this paper provides a much-needed circuit breaker to public agencies' prevailing treatment of risk and uncertainty and stimulates new lines of inquiry to improve the cost performance of transport 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 | 0.056 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.070 |
| Scholarly communication | 0.019 | 0.045 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".