convergence-multi-criteria-scoring

name	convergence-multi-criteria-scoring
description	Multi-Criteria Scoring Campaign — evaluate and rank candidates against multiple weighted criteria using AHP, BWM, TOPSIS, VIKOR, ELECTRE, PROMETHEE, MAUT methods.
execution	campaign
dependencies	{"strategies":["best-option-selection","category-sorting","full-ranking","non-compensatory-screening","weight-elicitation"],"campaigns":["pairwise-ranking"],"sops":["context-checkpoint","context-init","convergence-paper-overview","convergence-paper-research","convergence-paper-search","convergence-saturation-detection","convergence-sensitivity-analysis","convergence-web-research","convergence-web-search"]}

Multi-Criteria Scoring

Evaluate and rank candidate alternatives using multi-criteria weighted assessment. Supports mainstream MCDA methods including AHP, BWM, TOPSIS, VIKOR, ELECTRE, PROMETHEE, and MAUT, covering the full workflow from criteria definition, weighting, scoring, to sensitivity analysis.

Strategy Routing

Signal	Strategy
"What criteria? How to score?" / select best option / rank by criteria	best-option-selection
"Full ranking" / full ranking / league table / priority list	full-ranking
"Categorize" / categorize / A/B/C grading / compliance sorting	category-sorting
"Eliminate non-qualifying" / go/no-go / safety screening / veto	non-compensatory-screening
"Determine weights" / weight negotiation / criteria importance	weight-elicitation

Manifest

Strategies

Strategy	Purpose	Budget
best-option-selection	Select best option from candidates	M
full-ranking	Produce complete priority ranking	M
category-sorting	Classify candidates into predefined categories	M
non-compensatory-screening	Eliminate non-qualifying candidates	S
weight-elicitation	Determine criteria weights	S

Tactics

Tactic	Purpose
scoring-matrix-construction	Define criteria → assign weights → score → aggregate → sensitivity check
screening-then-scoring	Non-compensatory elimination first → then fine-grained scoring of survivors
multi-method-triangulation	Multi-method comparison → identify method-sensitive options

Subagent SOPs

SOP	Purpose
criterion-definition	Extract evaluation criteria from research goals and candidates
weight-elicitation-sop	Compute criteria weights using specified method
alternative-scoring	Score candidates against each criterion
normalization	Normalize the score matrix
dominance-check	Identify dominated and non-dominated alternatives
threshold-setting	Set minimum thresholds for each criterion
conjunctive-filter	Eliminate candidates failing thresholds
rank-comparison	Compare consistency of multiple ranking results
method-sensitivity-report	Analyze impact of method choice on rankings
scoring-synthesis	Synthesize scores, rankings, and sensitivity into final recommendation

Budget Table

Metric	Target
Criteria count	5-8
Candidate count	8-15
Weight method comparison	>=2 methods
Sensitivity analysis	>=3 parameter perturbations

MCP Tools

Tool	Usage
vault_search	Retrieve existing evaluation criteria and historical scores
vault_query_graph	Query relationships and dependencies between alternatives
vault_add_edge	Record evaluation result relationships

Context Management

• Each SOP executes as a subagent with independent context
• Strategy layer maintains State Ledger to track progress
• Score matrix is passed between SOPs as structured data
• Sensitivity analysis results are aggregated into final synthesis

Available Strategies

Optional, no fixed order; the final leaf is always a sop.

Strategy	When to use
best-option-selection	Select the single best candidate from a set using WSM, TOPSIS, AHP, MAUT, or VIKOR methods.
category-sorting	Classify candidates into predefined categories using ELECTRE-Tri, FlowSort, AHPSort, or DRSA methods.
full-ranking	Produce a complete ordering of all candidates using PROMETHEE I/II, ELECTRE III, or MAVT methods.
non-compensatory-screening	Eliminate non-qualifying candidates using conjunctive rules, dominance filtering, lexicographic ordering, or veto thresholds.
weight-elicitation	Determine criteria weights using AHP, Swing, BWM, MACBETH, or Simos methods.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
context-checkpoint	Append research process and results to the current Phase's context file. Each append MUST contain >=500 lines of markdown covering both process and results. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase.
context-init	Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.
convergence-paper-overview	Paper landscape scan at abstract level — discover MCDA, voting theory, Delphi, and optimization methodology papers.
convergence-paper-research	Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details.
convergence-paper-search	Paper AI summary reading — deeper understanding of specific methodology papers without full-text commitment.
convergence-saturation-detection	Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns.
convergence-sensitivity-analysis	Tests conclusion robustness by perturbing parameters and observing rank changes. Shared across scoring, portfolio, and steel-manning campaigns.
convergence-web-research	Deep web research with full-page extraction — detailed methodology guides, tutorials, implementation references.
convergence-web-search	Quick web scan to discover relevant pages — methodology references, case studies, best practices for convergence methods.

Available Campaigns

Optional, no fixed order; the final leaf is always a sop.

Campaign	When to use
pairwise-ranking	Pairwise Ranking Campaign — produce global rankings through pairwise comparisons and voting aggregation using Bradley-Terry, Elo, TrueSkill, Condorcet, Borda, Schulze methods.