Riscophobie et étalement à moyenne constante : analyse et applications
Bibliographic record
Abstract
L’objet de cet article est de montrer que, pour la perspective aléatoire caractérisée par la probabilité p de perdre une valeur h , il existe une mesure adéquate des variations dans le risque engendrées par des variations comparables de p et h , ce qui permet d’isoler le facteur risque et de prédire le comportement des agents riscophobes. Cette mesure résulte d’une application du concept d’étalement à moyenne constante ( mean-preserving spread ) développé par Rothschild et Stiglitz. Le résultat principal est à l’effet qu’un agent riscophobe préférera toujours une diminution de la perte à une baisse comparable de la probabilité de perte. Nous appliquons ce résultat simple à diverses situations : assurance-chômage, réglementation par enquêtes et amendes, contrôle des prix et des salaires, sécurité routière, stationnement illégal, loteries, autoassurance vs autoprotection. Enfin nous dérivons une mesure de variation compensatoire de richesse reliée au degré de riscophobie.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".