| name | stakeholder-weighted-ranking |
| description | Strategy: Weight by stakeholder perspective — the same gap carries different weight under different perspectives; take the consensus ranking at the end |
| version | 1.0.0 |
| category | hypothesis-formation |
| type | strategy |
| campaign | gap-prioritization |
| tactics | ["scoring-matrix-construction","priority-sensitivity-testing"] |
| sops | ["importance-scoring","feasibility-scoring","novelty-scoring","impact-scoring","ahp-weighting","priority-synthesis"] |
| dependencies | {"tactics":["hypothesis-formation-scoring-matrix-construction","priority-sensitivity-testing"],"sops":["gap-normalization"]} |
Stakeholder-Weighted Ranking
Rank with weights by stakeholder perspective: identify all relevant parties (researchers, engineers, policymakers, end users, etc.), construct an independent weight vector for each class of party, rank separately, and then take the consensus.
When to Use
- The research involves multiple stakeholders (e.g., medical AI: clinicians + patients + regulators)
- Different parties have fundamentally divergent definitions of "importance"
- A consensus must be built across parties, or the ranking differences across perspectives must be shown
- A funding agency or collaborator needs to see priorities from their own perspective
Thinking Framework
Core principle: there is no objective "most important gap", only "most important to whom".
The process has three layers:
Layer 1: Stakeholder identification
List all groups that would be affected by the research results. Each class of party has a different value function — engineers value feasibility, policymakers value impact, academic researchers value novelty.
Layer 2: Within-perspective ranking
For each class of party, use the same four-dimensional scoring as multi-criteria-ranking, but with a different weight vector. For example:
- Academic researchers: novelty 0.40, importance 0.30, impact 0.20, feasibility 0.10
- Engineers: feasibility 0.40, impact 0.30, importance 0.20, novelty 0.10
- Policymakers: impact 0.45, importance 0.35, feasibility 0.15, novelty 0.05
Layer 3: Consensus merging
Borda count or weighted-average the per-perspective rankings, identifying the "cross-perspective robust top gaps" (deemed important by all parties) and the "perspective-divergent gaps" (highly valued by some parties, ignored by others).
Key insight: perspective divergence is itself information — a gap with large divergence may need interest-alignment first, rather than a direct attack.
Budget Gate
| Tier | Gap count | Party count | Consensus method | Final output |
|---|
| S | 5–10 | 2–3 classes | Simple average | Per-perspective rankings + consensus top-3 |
| M | 11–20 | 3–5 classes | Borda count | Per-perspective rankings + consensus top-5 + divergence analysis |
| L | 20+ | 5+ classes | Weighted Borda + sensitivity |
