When is Bargaining Successful? Negotiated Division of Tournament Prizes
Bibliographic record
Abstract
We study bargaining at the end of high-stakes poker tournaments, in which participants often negotiate a division of the prize money rather than bear the risk of playing the game until the end. This setting is ideal for studying bargaining: the stakes are substantial, there are no restrictions on the negotiations or the terms of a deal, outside options are clearly defined, there are no agency conflicts, and there is little private information. Even in this setting, we find that risk-reducing deals often are not completed or even proposed. As expected, we find that players are more likely to negotiate when the gains to trade are large and when the coordination costs are lower. Surprisingly, although the likelihood of a successful deal is increasing in the stakes, this relation is driven only by the tournaments with the very largest prizes. It is also puzzling that the success of a proposal depends on who makes it, but initiating a proposal does not affect the proposing player's payoff in a completed deal. Divisions of prizes are closely related to players' outside options, while at the same time one of two focal points are often chosen. We also find intriguing differences between two-player deals and deals with three or more players.
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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 | 0.014 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".