Bibliographic record
Abstract
Abstract This study investigates the competition to be selected as the proposer in a subsequent multilateral bargaining game experimentally. The experimental environment varies in two dimensions: reservation payoffs (homogeneous or heterogeneous) and information on the extent of each subject's investment in the competition (public or private). The proposer's share was significantly lower than what theory predicts, and with taking into account the proposer's partial rent extraction, subjects over‐invest to increase their chances of winning the right to propose. More importantly, we find that inefficiency (due to the costly competition) and inequity go hand in hand; the surplus was distributed most efficiently and most equally when subjects were informed of who had spent how much in the competition, and slightly more when the reservation payoffs were heterogeneous. The proportion of proposals being rejected was smaller in the public treatments than in the private treatments. This study contributes to the literature by identifying formal rules that are more effective in establishing efficient informal norms.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".