Partage des coûts et tarification des infrastructures : enjeux, problématique et pertinence
Bibliographic record
Abstract
Most, if not all, organizations allocate common costs in one way or another among their various components or among their various partners. These common cost sharing problems are becoming increasingly acute as common cost sharing rules are important factors in competitiveness and performance. Although their explicit scientific analysis is already relatively advanced, their application within organizations (companies, alliances or business networks, governments) remains relatively embryonic and often dependent on an ad hoc historical approach, rather than rationally chosen to maximize organizational performance and value. We believe that organizations, broadly defined, would benefit from investing resources in learning common cost sharing methods that are more rigorous, efficient, equitable and incentive-based than those commonly used. We stress the importance of this investment in an economic context where the development of common infrastructures, both private and public, is omnipresent and conditions efficiency gains, which themselves have become the real cornerstone of competitiveness. We briefly present some cost sharing methods (Shapley-Shubik, nucleoli, sequential rule, ECPR, Ramsey-Boiteux, GPC). The study of these methods, which could better enhance the value of common infrastructures, is pursued in greater depth in the other documents in this series. Finally, we show how these methods have been or could be applied to nine problems that represent a much broader set of possible applications. La plupart des organisations, sinon toutes, repartissent d'une maniere ou d'une autre des couts communs entre leurs diverses composantes ou encore entre leurs differents partenaires. Ces problemes de partage de couts communs se posent avec de plus en plus d'acuite car les regles de partage des couts communs sont des facteurs importants de competitivite et de performance. Bien que leur analyse scientifique explicite soit deja relativement avancee, leur application au sein des organisations (entreprises, alliances ou reseaux d entreprises, gouver-nements) reste relativement embryonnaire et souvent tributaire d'une approche historique ad hoc, plutot que rationnellement choisie pour maximiser la performance et la valeur de l'organisation. Nous croyons que les organisations, entendues au sens large, auraient interet a investir des ressources dans l'apprentissage de methodes de partage de couts communs plus rigoureuses, plus efficaces, plus equitables et plus incitatives que celles couramment utilisees. Nous insistons sur l'importance de cet investissement dans un contexte economique ou le developpement d'infrastructures communes, tant privees que publiques, est omnipresent et conditionne les gains d efficacite, devenus eux-memes la veritable pierre angulaire de la com-petitivite. Nous presentons brievement certaines methodes de partage de couts (Shapley-Shubik, nucleole, regle sequentielle, ECPR, Ramsey-Boiteux, GPC). L'etude de ces methodes, susceptibles de mieux valoriser les infrastructures communes, est poursuivie plus en profondeur dans les autres documents de la presente serie. Nous montrons enfin comment ces methodes ont ete ou pourraient etre appliquees a neuf problemes representatifs d'un ensemble beaucoup plus vaste d'applications possibles.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".