yogsoth-ai Screening Then Scoring
First eliminate non-qualifying alternatives using non-compensatory rules, then perform fine-grained scoring and ranking of survivors. Suitable for scenarios with large candidate sets or hard constraints.
Stages
- Threshold Setting — Set minimum thresholds for critical criteria
- Conjunctive Filtering — Eliminate non-qualifying alternatives based on thresholds
- Dominance Check — Identify dominated alternatives among survivors (optional further reduction)
- Full Scoring — Execute scoring-matrix-construction workflow on survivors
Available SOPs
- threshold-setting — Set criteria thresholds
- conjunctive-filter — Conjunctive rule elimination
- dominance-check — Dominance relationship check
- criterion-definition — Fine-grained scoring criteria (Stage 4)
- weight-elicitation-sop — Weight computation (Stage 4)
- alternative-scoring — Alternative scoring (Stage 4)
- normalization — Normalization (Stage 4)
- scoring-synthesis — Comprehensive recommendation (Stage 4)
Execution Guidance
- Stages 1-3 are the screening phase, rapidly reducing the candidate set
- Screening criteria should focus on hard constraints (safety, compliance, budget caps, etc.)
- Survivor count should ideally be 3-8 before entering fine-grained scoring
- If too few survivors after screening (<3), consider relaxing thresholds
- If too many survivors (>10), add screening criteria or tighten thresholds
- Stage 4 reuses the complete scoring-matrix-construction tactic workflow
Minimum Yield
Elimination rationale + survivor ranking
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. |
| conjunctive-filter | Apply conjunctive screening rules to eliminate candidates that fail any threshold. |
| criterion-definition | Extract evaluation criteria from research goals and candidate alternatives. |
| dominance-check | Identify dominated and non-dominated alternatives in a score matrix using Pareto dominance. |
| 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. |
| threshold-setting | Define minimum acceptable thresholds for each criterion based on context and constraints. |
| weight-elicitation-sop | Compute criteria weights using a specified elicitation method (AHP, Swing, BWM, MACBETH, or Simos). |