Changement d'avis des préférences des décideurs assisté par un modèle multicritère
Bibliographic record
Abstract
Often, the decision-maker does not have consistent knowledge about the problems to be judged. Consequently, he provides answers without calculation, non-stereotyped, rather dynamic therefore. We believe that it is high time that we look for a mathematical formulation that can assist the dynamic thinking of the decision-maker in a state of limited rationality. This is why we proposed a model of temporal evolution of weights and other decision parameters, then we validated it using a confidential project of investment in urban infrastructure, and an evolution of consumption in France. This model was used to simulate consumption a few years later. We have not recalled all the calculations related to the use of the flow balance method because they have often been explained in the literature. We simply presented the inputs since they are the ones that are likely to evolve over time. Subsequently, we propose a possibilist form of flow balances, the interest of which is to reduce by one the number of parameters per criterion that the decision-maker must evaluate. Indeed, these parameters are difficult to quantify, especially the indifference, preference and veto thresholds because the weight of each criterion must correspond to the preferences of the decision-maker. This is why we then propose a method for determining thresholds for flow balances. It is not a question of replacing the decision-maker, but on the contrary of facilitating the complicated task of evaluating thresholds, since the final choice is up to him.
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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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".