Bibliographic record
Abstract
While there is an extensive literature on how economic agents bargain to divide an asset, little is known about the decision to initiate bargaining and how the initiation affects the outcome of bargaining. We address these questions in the context of high-stakes poker tournaments in which the last few players often negotiate the division of the remaining prize money rather than risk playing the tournament to the end. In 63% of the tournaments in our sample players enter into negotiations, and in 31%, they successfully reach an agreement. We find that the identity of the player who initiates bargaining affects whether a deal is completed but does not affect the terms of the eventual deal. The initiator tends to have a weaker than average position at the table, but the likelihood that a deal will be completed increases in the initiator's strength in the game and history of winning past tournaments. These findings indicate that initiating negotiations conveys information that is relevant to whether a deal will emerge. Nevertheless, initiating bargaining does not affect the initiator's pay-off in a completed deal. Lastly, we find strong evidence that bargaining tends to be initiated and is more likely to be successful when participants' stakes are about equal, consistent with the theoretical work of Cramton, Gibbons and Klemperer (1987, “Dissolving a Partnership Efficiently”, Econometrica, 55, 615–632).
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".