Bias versus error: why projects fall short
Bibliographic record
Abstract
Purpose Worldwide, major projects often make the headlines as they suffer from a fourfold whammy of delays, cost blowouts, benefit shortfalls and stakeholder disappointments. It seems that error and bias can explain their underperformance. Which overarching explanation outweighs the other? It is the question this paper aims to address. Design/methodology/approach Insights are garnered from decades of research on thousands of major projects in developed and developing countries worldwide. In particular, two high-profile project cases, the Veteran Affairs Hospital in Aurora, Colorado (USA) and the Philharmonie de Paris (France), are explored. Findings The case projects show that error and bias combine to best explain project (under) performance. Applying best practices or debiasing project cost and benefit estimates is insufficient to prevent cost blowouts and benefit shortfalls. The confrontation of the two overarching explanations is not merely platonic. It is real and may lead to a media and legal battle. Originality/value This viewpoint calls practitioners to transcend the error versus bias debate and reconcile two key characters in the world of major projects: the “overoptimistic” who hold a bias for hope and firmly believe that, despite error down the road, many projects would, in the end, “stumble into success” as creativity may come to the rescue; and the “overpessimistic” who hold a bias for despair and think many projects should not have been started.
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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 | 0.160 | 0.462 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".