The "threat premium" in economic bargaining and who pays the price
Bibliographic record
Abstract
What information do people use when deciding to be fair or exploitative in face-to-face bargaining interactions? We show that people extract information about threat potential from a stable cue in the stranger's face, the facial width-to-height ratio (fWHR), and dynamically adjust their bargaining behaviour based on this information. In a modified Ultimatum Game, participants (n = 100) gave larger offers to men (n = 48) with larger fWHRs (r = .46), an effect driven by the tendency to view such men as more aggressive than those with smaller fWHRs. This "threat premium" was most pronounced for male proposers who were physically weaker, and was at odds with, and suppressed, the tendency to pay attractive individuals more than unattractive individuals. Therefore, threat potential appears to guide economic interactions involving unrelated strangers, and this effect overrides any inclinations to favour those who are more attractive. Meeting abstract presented at VSS 2016
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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 | 0.034 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".