Bibliographic record
Abstract
Le but de cet article est d’étudier le contrat d’assurance optimal dans un contexte où un assuré est soumis à de l’anti-sélection (qu’on appelle aussi dans ce contexte aléa moral ex post ) et où un assureur est incapable de s’engager pleinement dans une stratégie de vérification. Le problème étudié se rapproche grandement de celui lié à la fraude à l’assurance où seul l’assuré connaît à coût nul l’état de la nature (s’il a subi un sinistre ou non). En modélisant le comportement de l’assureur et de l’assuré comme un jeu non coopératif, nous démontrons que les contrats d’assurance comportant une clause de valeur à neuf sont optimaux. Ces contrats, qui surindemnisent un assuré en cas de sinistre, permettent à l’assureur d’envoyer à l’assuré un signal crédible qu’il vérifiera avec une plus grande probabilité les réclamations de ce dernier.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".