Testosterone and Cortisol Jointly Predict the Ambiguity Premium in an Ellsberg-Urns Experiment
Bibliographic record
Abstract
Previous literature has tried to establish whether and how steroid hormones are related to economic risk-taking. In this study, we investigate the relationship between testosterone (T) and cortisol (C) on one side and attitudes toward risk and ambiguity on the other. We asked 78 male undergraduate students to complete several tasks and provide two saliva samples. In the task "Reveal the Bag," participants expressed their beliefs on an ambiguous situation in an incentivized framework. In the task "Ellsberg Bags," we elicited from the participants through an incentive-compatible mechanism the reservation prices for a risky bet and an ambiguous bet. We used the difference between the two prices to calculate each participant's ambiguity premium. We found that participants' salivary T and C levels jointly predicted the ambiguity premium. Participants featuring comparatively lower levels of T and C showed the highest levels of ambiguity aversion. The beliefs expressed by a subset of participants in the "Reveal the Bag" task rationalize (in a revealed preference sense) their choices in the "Ellsberg Bags" task.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".