Bibliographic record
Abstract
Abstract We study optimal contracts in environments where a risk‐averse supplier discovers cost information privately and gradually over time: the supplier is privately informed about its cost uncertainty at the time of contracting and discovers the realization of cost condition privately after contracting and before production. We show that both the buyer and the supplier prefer more cost uncertainty when the supplier is not very risk‐averse but less cost uncertainty when the supplier is sufficiently risk‐averse. However, the buyer always prefers to contract before the cost uncertainty resolves regardless of the supplier's degree of risk aversion. The nature of the optimal contract also depends on the supplier's risk preference. A separating contract is optimal when the supplier is not very risk‐averse; however, a pooling contract, which offers the same contract terms regardless of the cost uncertainty, can be optimal when the supplier becomes sufficiently risk‐averse. Moreover, the optimal production schedule is often characterized by “inflexible rules.”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".