Contract Structure for Joint Production: Risk and Ambiguity under Compensatory Damages
Bibliographic record
Abstract
at various stages of this project. We also thank an anonymous AE and two anonymous reviewers for their patience, several careful readings, and a wealth of thoughtful feedback. We develop a model in which the parties to a joint production project have a choice of specifying contractual performance in terms of actions or deliverables. Penalties for noncompliance are not specified; rather, they are left to the courts under the legal doctrine of compensatory damages. We analyze three scenarios of increasing uncertainty: Full Knowledge – the implications of partner actions are known; Risk – the implications can be probabilistically quantified; and, Ambiguity – the implications cannot be so quantified. In Full Knowledge, action requirements dominate: they always induce the maximum economic value. This dominance vanishes in Risk. In Ambiguity, deliverables specifications can interact with compensatory damages to create a form of “ambiguity insurance ” – ambiguity aversion is assuaged in a way that increases the aggregate, subjective expected value of the project. This effect does not arise under action requirements. Thus, deliverables contracts may facilitate highly novel joint projects that would otherwise be foregone due to excessive uncertainty. 1
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 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".