PROMETHEE-MP: a generalisation of PROMETHEE for multi-period evaluations under uncertainty
Bibliographic record
Abstract
Sustainability is a major concern and decisions have to be made based on the triple bottom line while simultaneously evaluating the economic, social and environmental impacts. In this context, decisions generally have a planning horizon of several years or even decades and consequently need to be evaluated in the short, medium and long term under uncertainty. This paper intends to tackle this complexity and proposes a multi-period generalisation of PROMETHEE under uncertainty, named PROMETHEE-MP, which is based on a double aggregation (a multi-criteria aggregation and a temporal aggregation), followed by an exploitation phase. The multi-criteria aggregation step uses a generalisation of PROMETHEE III in a situation of random uncertainty with intervals generated by Monte Carlo simulation. For temporal aggregation, we use a measure of distance between pre-orders that captures indifference, strict preference, weak preference and incomparability relations. Finally, we illustrate the proposed PROMETHEE-MP in the context of sustainable forest management.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".