pairwise-ranking

Productivity

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

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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. 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-saturation-detection	Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns.