Settlement Escrows: An Experimental Study of a Bilateral Bargaining Game
Bibliographic record
Abstract
This paper reports the results of a bargaining experiment. We follow the pretrial bargaining model of Gertner and Miller (1995) under uncertainty and examine the effect of a litigation institution, called a settlement escrow and uncertainty on the timing and quality of settlement outcomes. Our findings indicate that settlement rates are significantly higher when a settlement escrow is added to the bargaining process where there is asymmetric information. Quality of outcomes, measured as the percentage of the true damage that the outcome represents, is positively and significantly influenced by the addition of a settlement escrow. Settlement rates are higher when certainty is provided, under the no escrow institution. Quality of outcomes is negatively and significantly influenced by the addition of certainty. The escrow institution has no effect on bargaining outcomes, under the certainty condition; and, the provision of certainty has no effect on bargaining outcomes, under the escrow institution. These findings suggest first that the escrow is a useful device for improving efficiency when bargaining is conducted under uncertainty and second, that the escrow fully compensates the negative effect of uncertainty on bargaining processes.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".