Renegotiation of long‐term contracts as part of an implicit agreement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
I study a repeated principal–agent game with long‐term output contracts that can be renegotiated at will. Actions are observable but not contractible, so they can only be incentivized through implicit agreements formed in equilibrium. I show that contract renegotiation is a powerful tool for incentive provision, despite the stationarity of the environment. Continuation contracts are designed to punish deviations in noncontractible behavior. If the equilibrium actions are observed, these contracts are renegotiated away. This form of anticipated renegotiation results in welfare improvements over outcomes attainable by one‐period contracts or by long‐term contracts that are not renegotiated. When the principal is not protected by limited liability, first‐best outcomes are attainable regardless of the impatience of the players. Equilibrium strategies are shown to satisfy various concepts of renegotiation‐proofness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it