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convergence-scoring-matrix-construction

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Build a complete scoring matrix through criterion definition, weighting, scoring, normalization, and sensitivity testing.

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/convergence-scoring-matrix-construction/SKILL.md

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Scoring Matrix Construction

Standard MCDA workflow of define criteria → assign weights → score → aggregate → sensitivity check, producing a complete scoring matrix and ranking results.

Stages

  1. Criterion Definition — Extract evaluation criteria from research goals and candidates
  2. Weight Elicitation — Compute criteria weights using specified method
  3. Alternative Scoring — Score candidates against each criterion
  4. Normalization — Normalize scores to make different scales comparable
  5. 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.

SOPWhen to use
alternative-scoringScore each candidate alternative against all criteria to produce a score matrix.
criterion-definitionExtract evaluation criteria from research goals and candidate alternatives.
normalizationNormalize a score matrix using a specified method to make scores comparable across criteria.
scoring-synthesisSynthesize score matrix, rankings, and sensitivity analysis into a final recommendation.
weight-elicitation-sopCompute criteria weights using a specified elicitation method (AHP, Swing, BWM, MACBETH, or Simos).