Talent Selection Under Uncertainty: Exploring Cognitive Biases Through the Lens of a Professional Basketball Entry Draft
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Every year, professional basketball franchises convene for an entry draft to select the next generation of talent for their respective teams. This article describes an experiential exercise that places students in the role of team executives of a fictionalized professional basketball franchise. Students are tasked with evaluating a group of draft-eligible athletes and making the optimal selection for their team, considering a wide array of skills, abilities, and attributes. The Basketball Draft Night Exercise is informed by extensive research on sport entry drafts spanning several decades and sheds light on the systematic errors, fallacies, and decision-making biases that arise when making talent selections under uncertainty. Furthermore, this exercise illuminates the potential pitfalls and cognitive errors committed by experts when assessing and selecting talent under uncertainty.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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