Bibliographic record
Abstract
The Planning Fallacy principle, a prominent account of project behavior and particularly the causes of cost overruns and benefit shortfalls, stems from the belief that bias outweighs error. Its very popularity begs for an inquiry on its theoretical appeal and whether it remains a viable argument to explain cost overrun and benefit shortfall behavior. This chapter argues that the Planning Fallacy is in danger of danger of being debunked, suggesting that while its rise can be attributed to an impulsion to theorize, its subsequent fall may be due to a companion compulsion to theorize. The chapter focuses on three questions: Is the Planning Fallacy principle a theory anyway and, if yes, is the theory complete? Is the theory too narrow in scope or does it cater to complexity and uncertainty? Will it lose support from the scientific community? The chapter demonstrates that the fall may be due to the incomplete nature of the theory and its limited scope. The chapter contends that the popular appeal of the Planning Fallacy has vastly overstepped its ultimate viability and suggests that the Planning Fallacy principle is itself a form of Planning Fallacy as it overestimates its own theoretical power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".