stakeholder-weighted-ranking

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