efficient-exploration

Research

Strategy for large-N sparse pairwise comparison using TrueSkill, active learning, and rank centrality to rank 100+ candidates from limited comparisons.

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Efficient Exploration

Purpose

Produce reliable rankings when the candidate set is too large for complete comparison. Uses information-theoretic pair selection and sparse-matrix rating algorithms to converge quickly with minimal comparisons.

When to use

Candidate count N ≥ 100
Complete comparison infeasible (budget << N(N-1)/2)
Approximate ranking acceptable — top-k identification sufficient
Speed/efficiency prioritized over perfect calibration

Budget

Resource	Allocation
Comparisons	N×log(N) to 3N×log(N)
Iterations	5-20 rounds of adaptive selection
Convergence target	Top-k stability ≥ 90% for 3 consecutive rounds

State Ledger

candidates: []          # full candidate list
comparison_history: []  # [{pair, winner, confidence, round}]
ratings: {}             # candidate → {mu, sigma}
method: ""              # trueskill | bt-incomplete | rank-centrality
iteration: 0
budget_remaining: 0
convergence: {stable: false, score: 0.0, top_k_stable: false}

Available Tactics

adaptive-pair-selection — maximize information gain per comparison
consistency-audit-loop — spot-check transitivity in top-k region

Available SOPs

pair-selector
comparison-executor
rating-update
convergence-check
cycle-detection
ranking-synthesis

Execution Guidance

Initialize all candidates with prior (mu=25, sigma=8.33 for TrueSkill)
Run adaptive-pair-selection with uncertainty-based pair selection
Prioritize comparisons that reduce uncertainty in top-k boundary
Check convergence every N/10 comparisons
When budget exhausted or converged, run ranking-synthesis
Optional: spot-check consistency in top-10 region

Output Format

ranking:
  - {rank: 1, candidate: "...", mu: 38.2, sigma: 1.4, ci: [35.4, 41.0]}
  - {rank: 2, candidate: "...", mu: 36.8, sigma: 1.6, ci: [33.6, 40.0]}
method: trueskill
total_comparisons: 847
budget_utilization: 0.92
top_10_stability: 0.96
convergence_round: 14

Available Tactics

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

Tactic	When to use
adaptive-pair-selection	Iteratively select maximally informative pairs, execute comparisons, update ratings, and check convergence until ranking stabilizes.
consistency-audit-loop	Detect preference cycles, localize inconsistent judgments, request corrections, and recompute ratings until consistency threshold is met.

Available SOPs

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

SOP	When to use
ranking-synthesis	Produce the final ranking artifact from converged ratings and consistency report.