Trial and Settlement: A Study of High-Low Agreements
Bibliographic record
Abstract
This paper presents the first systematic theoretical and empirical study of high-low agreements in civil litigation.A high-low agreement is a private contract that, if signed by litigants before the conclusion of a trial, constrains any plaintiff recovery to a specified range.Whereas existing work describes litigation as a choice between trial and settlement, our examination of high-low agreements-an increasingly popular phenomenon in civil litigation-introduces partial or incomplete settlements.In our theoretical model, trial is both costly and risky.When litigants have divergent subjective beliefs and are mutually optimistic about their trial prospects, cases may fail to settle.In these cases, high-low agreements can be in litigants' mutual interest because they limit the risk of outlier awards while still allowing an optimal degree of speculation.Using claims data from a national insurance company, we describe the features of these agreements and empirically investigate the factors that may influence whether litigants discuss or enter into them.Our empirical findings are consistent with the predictions of the theoretical model.We also explore extensions and alternative explanations for high-low agreements, including their use to mitigate excessive, offsetting trial expenditures and the role that negotiation costs might play.Other applications include the use of collars in mergers and acquisitions.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".