Using PROMETHEE Method for Multi-Criteria Decision Making: Applications and Procedures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) is one of the main MCDM methods helping decision-makers to investigate a set of alternatives considering different criteria. This method is particularly useful when the decision-makers need to compare a set of alternatives based on multiple criteria. The PROMETHEE method has been applied in various fields, including business, finance, hydrology, and water management. In business, for instance, PROMETHEE can be used to evaluate different investment opportunities based on various criteria such as return on investment, risk, and strategic fit. In water management, PROMETHEE can be used to evaluate alternative strategies for water allocation or pollution control, considering factors such as environmental impact, cost, and social acceptability. Different versions of PROMETHEE have been developed, each with its own specific characteristics and requirements. This paper describes the steps of the PROMETHEE I and II procedures, which are among the most widely used versions of the method. The PROMETHEE I procedure is used for ranking alternatives based on a single criterion, while PROMETHEE II is used for ranking alternatives based on multiple criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it