yogsoth-ai Scoring Matrix Construction
Standard MCDA workflow of define criteria → assign weights → score → aggregate → sensitivity check, producing a complete scoring matrix and ranking results.
Stages
- Criterion Definition — Extract evaluation criteria from research goals and candidates
- Weight Elicitation — Compute criteria weights using specified method
- Alternative Scoring — Score candidates against each criterion
- Normalization — Normalize scores to make different scales comparable
- Sensitivity Testing — Perturb weights to verify result robustness
Available SOPs
- criterion-definition — Extract evaluation criteria
- weight-elicitation-sop — Compute criteria weights
- alternative-scoring — Score alternatives
- normalization — Normalize scores
- scoring-synthesis — Aggregation and sensitivity (including perturbation analysis)
Execution Guidance
- Stages execute sequentially; each stage's output serves as input for the next
- Stage 2 method options: AHP, Swing, BWM, MACBETH, Simos
- Stage 4 normalization method must match the subsequent aggregation method (linear normalization for WSM, vector normalization for TOPSIS)
- Sensitivity testing must perturb at least 3 weight parameters by ±10%
- If any stage's output fails quality requirements, roll back and redo that stage
Minimum Yield
Complete scoring matrix + weight vector + ranking results
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| alternative-scoring | Score each candidate alternative against all criteria to produce a score matrix. |
| criterion-definition | Extract evaluation criteria from research goals and candidate alternatives. |
| normalization | Normalize a score matrix using a specified method to make scores comparable across criteria. |
| scoring-synthesis | Synthesize score matrix, rankings, and sensitivity analysis into a final recommendation. |
| weight-elicitation-sop | Compute criteria weights using a specified elicitation method (AHP, Swing, BWM, MACBETH, or Simos). |