Casting a long shadow: On the death and abiding influence of Daniel Kahneman in shaping project management theory and practice
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it