For the Win: Risk‐Sensitive Decision‐Making in Teams
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.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".