Casting a long shadow: On the death and abiding influence of Daniel Kahneman in shaping project management theory and practice
Bibliographic record
Abstract
• Essay offers festschrift on Daniel Kahneman's contributions to project management • Essay reflects on why Kahneman's work resonates within project management • Essay takes stock on Kahneman's work on judgment and decision-making under risk • Essay takes stock on Kahneman's work on bias, error and noise • Essay takes stock on Kahneman's work on the Planning Fallacy With the recent passing of Daniel Kahneman, the Nobel Prize winner, the opportunity to offer reflection on his contributions to project management theory and practice is timely. Indeed, while Kahneman himself is no longer with us, his ideas are long lived in the project management field. This essay is offered as a festschrift and is accompanied by invited commentaries. We take stock of Kahneman's work on judgment and decision-making under risk; bias, error and noise; and the Planning Fallacy. We note that much of his work served as a foreshadowing of current scholarship and avenues for exploration in project management; everything from topics such as project behavior to causes and effects of project performance, not to mention AI as well as happiness and well-being in projects. We argue that Kahneman's ideas at the intersection of psychology and economics did not so much revolutionize as upend our understanding of project management.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".