For the Win: Risk‐Sensitive Decision‐Making in Teams
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
Abstract Risk‐sensitivity theory predicts that decision‐makers should prefer high‐risk options in high need situations when low‐risk options will not meet these needs. Recent attempts to adopt risk‐sensitivity as a framework for understanding human decision‐making have been promising. However, this research has focused on individual‐level decision‐making, has not examined behavior in naturalistic settings, and has not examined the influence of multiple levels of need on decision‐making under risk. We examined group‐level risk‐sensitive decision‐making in two American football leagues: the National Football League (NFL) and the National College Athletic Association (NCAA) Division I. Play decisions from the 2012 NFL (Study 1; N = 33 944), 2013 NFL (Study 2; N = 34 087), and 2012 NCAA (Study 3; N = 15 250) regular seasons were analyzed. Results demonstrate that teams made risk‐sensitive decisions based on two distinct needs: attaining first downs (a key proximate goal in football) and acquiring points above parity. Evidence for risk‐sensitive decisions was particularly strong when motivational needs were most salient. These findings are the first empirical demonstration of team risk‐sensitivity in a naturalistic organizational setting. Copyright © 2016 John Wiley & Sons, Ltd.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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