La modélisation des avantages au personnel : complexité et limites du modèle actuariel,le rôle majeur des comportements humains
Bibliographic record
Abstract
L’étude porte sur la modélisation financière d’avantages accordés au personnel d’une entreprise. Elle montre, en plusieurs étapes, la manière de construire un modèle actuariel dont l’objectif est de calculer le poids financier des engagements sociaux accordés par une entreprise. La finalité est de comptabiliser, à sa juste valeur, les provisions financières correspondantes en vertu des normes comptables internationales (IFRS) définissant les avantages au personnel (IAS 19). Elle est illustrée par un exemple concret, celui de l’accord collectif de cessation progressive d’activité mis en place à la SNCF en 2008 ( source : Conseil d’Orientation des Retraites, document n°10, 11/02/2009 ). Il s’agit de mettre en lumière la complexité inhérente d’une telle modélisation à la fois en termes de méthode et au choix de modélisation, ainsi que les limites issues des hypothèses retenues (formalisation théorique et calibration). La complexité et le rôle majeur de la connaissance des comportements humains sont soulignés au regard de l’appréciation de la confiance à accorder aux estimations financières obtenues.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".