Comparison of Analytic Hierarchy Process and Dominance-Based Rough Set Approach as Multi-Criteria Decision Aid Methods for the Selection of Investment Projects
Bibliographic record
Abstract
This investigation compares two multi-criteria analysis methods, Analytic Hierarchy Process (AHP) and Dominance- based Rough Set Approach (DRSA), applied to the ranking of ten investment projects based on evaluation of the overall risk associated with each. AHP requires decision makers to evaluate the various elements of risk by paired comparison in terms of their impact on the element above them in the hierarchy. Each investment project is then rated in terms of each risk to produce a weighted summation used for ranking purposes. DRSA produces a ranking based on a set of decision rules that are derived from evaluation of a reduced number of reference projects well known to the decision makers. For this purpose, four reference projects were chosen from the ten. The results show that the two methods gave very similar final rankings of the ten projects. The advantage of DRSA is that the projects are evaluated using a reduced number of attributes without explicit knowledge of their impact in the hierarchy, thus eliminating a lengthy and tedious process for the decision makers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".