Coûts de l’autoprotection et équilibre d’un marché de l’assurance concurrentiel
Bibliographic record
Abstract
Nous considérons un marché concurrentiel de l’assurance en présence d’aléa moral dans lequel le niveau du coût de l’autoprotection de l’assuré est son information privée. Nous caractérisons alors l’équilibre en contrats du marché en supposant qu’il existe deux types d’agents : un type à faible coût marginal de l’effort et un autre dont le coût marginal est élevé. D’une part, nous montrons que le niveau de l’autoprotection de l’agent à l’équilibre est le même que dans la situation d’aléa moral pur. D’autre part, nous montrons qu’à l’équilibre les agents dont le coût marginal de l’effort est élevé obtiennent le même contrat que dans la situation d’aléa moral pur alors que l’autre type d’agents peut obtenir une couverture d’assurance plus faible. Enfin, l’existence de l’équilibre n’est pas toujours garantie.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".