Renegotiation of long‐term contracts as part of an implicit agreement
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.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".