pairwise-ranking

name	pairwise-ranking
description	Pairwise Ranking Campaign — produce global rankings through pairwise comparisons and voting aggregation using Bradley-Terry, Elo, TrueSkill, Condorcet, Borda, Schulze methods.
execution	campaign
dependencies	{"strategies":["coherence-diagnosis","collective-adjudication","deliberative-calibration","dynamic-tracking","efficient-exploration"],"sops":["context-checkpoint","context-init","convergence-saturation-detection"]}

Pairwise Ranking

Produce global rankings from pairwise comparisons. This campaign orchestrates comparison collection, rating computation, multi-judge aggregation, and consistency verification to yield robust ordinal rankings with confidence estimates.

Strategy Routing

Signal	Strategy
Small N precise comparison / 5-15 options / careful calibration	deliberative-calibration
Large N sparse / 100+ options / can only compare subset	efficient-exploration
Multi-judge / committee / multi-judge / LLM judge aggregation	collective-adjudication
Continuous update / Elo / live rating / A/B testing	dynamic-tracking
Consistency audit / cycle detection / transitivity check	coherence-diagnosis

Manifest

Strategies

Strategy	Methods	When
deliberative-calibration	Bradley-Terry, Thurstone, AHP pairwise, Borda	Small N complete comparison
efficient-exploration	BT incomplete, TrueSkill, Active learning, Rank Centrality	Large N sparse matrix
collective-adjudication	Condorcet/Schulze, Borda, Kemeny-Young, Copeland	Multi-judge aggregation
dynamic-tracking	Elo, Glicko-2, TrueSkill 2, Whole-History Rating	Continuous rating update
coherence-diagnosis	Consistency Ratio, cycle enumeration, mElo	Preference consistency check

Tactics

Tactic	Purpose
adaptive-pair-selection	Iteratively select maximally informative pairs, compare, update ratings, check convergence
multi-judge-aggregation	Collect independent ballots from multiple judges, aggregate, identify disagreement
consistency-audit-loop	Detect cycles, localize inconsistencies, request corrections, recompute

SOPs

SOP	Input	Output
pair-selector	current_ratings, comparison_history	next_pairs[]
comparison-executor	pair, context	judgment(winner, confidence, reasoning)
rating-update	judgment, current_ratings, method	updated_ratings
convergence-check	rating_history	converged(bool), stability_score
ballot-collection	candidates[], perspectives[]	ballots[]
aggregation-method	ballots[], method	consensus_ranking
cycle-detection	comparison_matrix	cycles[], transitivity_score
inconsistency-localization	comparison_matrix, cycles[]	problematic_pairs[]
ranking-synthesis	ratings, consistency_report	final_ranking

Budget Table (M tier)

Dimension	Threshold
Comparison pairs	>= N*log(N) pairs (N=candidate count)
Judge count (collective)	>=3 independent perspectives
Consistency check	CR < 0.1 or equivalent threshold
Convergence criterion	ranking stability >= 90%

MCP Tools

• mcp__wiki-vault__vault_search — retrieve candidate descriptions and prior rankings
• mcp__wiki-vault__vault_add_edge — record ranking relationships
• mcp__wiki-vault__vault_query_graph — check existing preference edges

Context Management

• State is maintained in a ranking_state ledger passed between tactics
• Each SOP receives only its required inputs (no full state leakage)
• Convergence check gates iteration termination
• Final synthesis produces the deliverable ranking artifact

Available Strategies

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

Strategy	When to use
coherence-diagnosis	Strategy for auditing preference consistency using Consistency Ratio, cycle enumeration, and mElo to detect and resolve intransitivities.
collective-adjudication	Strategy for multi-judge ranking aggregation using Condorcet, Schulze, Borda, Kemeny-Young, and Copeland methods to produce consensus rankings from diverse perspectives.
deliberative-calibration	Strategy for small-N complete pairwise comparison using Bradley-Terry, Thurstone, AHP, and Borda methods to produce calibrated rankings.
dynamic-tracking	Strategy for continuous rating updates using Elo, Glicko-2, TrueSkill 2, and Whole-History Rating for live ranking systems and A/B testing.
efficient-exploration	Strategy for large-N sparse pairwise comparison using TrueSkill, active learning, and rank centrality to rank 100+ candidates from limited comparisons.

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. Covers both process and results with genuine substance. 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-saturation-detection	Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns.