Inside the Planning Fallacy: The Causes and Consequences of Optimistic Time Predictions
Bibliographic record
Abstract
Individuals, organizations, and governments all commonly plan projects and estimate when they will be completed. Kahneman and Tversky (1979) suggested that such estimates tend to be optimistic because planners rely on their best-case plans for a current project even though similar tasks in the past have typically run late. As a result of this planning fallacy, predicted completion times for specific future tasks tend to be more optimistic than can be justified by the actual completion times or by the predictors' general beliefs about the amount of time such tasks usually take. Anecdotal evidence of the planning fallacy abounds. The history of grand construction projects is rife with optimistic and even unrealistic predictions (Hall, 1980), yet current planners seem to be unaffected by this bleak history. One recent example is the Denver International Airport, which opened 16 months late in 1995 with a construction-related cost overrun of $3.1 billion; when interest payments are added, the total cost is 300% greater than initially projected. The Eurofighter, a joint defense project of a number of European countries, was scheduled to go into service in 1997 with a total project cost of 20 billion Eurodollars; it is currently expected to be in service in 2002 with a total cost of some 45 billion Eurodollars. One of the most ambitious regional mega-projects is Boston's Central Artery/Tunnel expressway project, originally scheduled to open in 1999. The project is currently forecast to open 5 years late and double the original budget.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".