Overcompensation as a Partial Solution to Commitment and Renegotiation Problems: The Case of <i>Ex Post</i> Moral Hazard
Bibliographic record
Abstract
Abstract In a Costly State Verification world, an agent who has private information regarding the state of the world must report what state occurred to a principal, who can verify the state at a cost. An agent then has what is called ex post moral hazard: he has an incentive to misreport the true state to extract rents from the principal. Assuming the principal cannot commit to an auditing strategy, the optimal contract is such that: (1) the agent's expected marginal utility when there is an accident (high‐ and low‐loss states) is equal to his marginal utility when there is no accident; (2) the lower loss is undercompensated, while the higher loss is overcompensated; and (3) the welfare of the agent is greater under commitment than under no‐commitment. Result 2 is contrary to the results obtained if the principal can commit to an auditing strategy (higher losses underpaid and lower losses overpaid). The reason is that by increasing the difference between the high and the low indemnity payments, the probability of fraud is reduced.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".