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 | >= Nlog(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

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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. |

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