multi-judge-aggregation

Research

Collect independent rankings from multiple judges, aggregate using social choice methods, and identify disagreement hotspots.

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Multi-Judge Aggregation

Collect independent ballots from multiple judges or perspectives, aggregate them into a consensus ranking using social choice theory, and surface disagreement patterns for further investigation.

Stages

Collect — ballot-collection gathers independent rankings from each judge/perspective
Aggregate — aggregation-method applies social choice method to produce consensus
Audit — cycle-detection checks for Condorcet cycles in the aggregated preference matrix

Available SOPs

Stage	SOP	Input	Output
Collect	ballot-collection	candidates[], perspectives[]	ballots[]
Aggregate	aggregation-method	ballots[], method	consensus_ranking
Audit	cycle-detection	comparison_matrix	cycles[], transitivity_score

Execution Guidance

Ensure judges evaluate independently (no anchoring or information leakage)
Use ≥3 judges for meaningful aggregation
Default to Schulze method (satisfies many desirable social choice properties)
Cross-validate with Borda count as sanity check
Flag pairs where judge agreement < 60% as disagreement hotspots
If Condorcet cycles exist, report them explicitly — do not silently resolve

Minimum Yield

Consensus ranking + disagreement heatmap
Consensus ranking with method used and confidence
Disagreement heatmap: for each pair, what fraction of judges agree
Condorcet winner identification (or explicit statement of cycle)
Per-judge deviation from consensus (who disagrees most, on what)

Available SOPs

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

SOP	When to use
aggregation-method	Aggregate multiple ranking ballots into a consensus ranking using a specified social choice method.
ballot-collection	Gather independent ranking ballots from multiple judges or perspectives for a given candidate set.
cycle-detection	Scan a pairwise comparison matrix for preference cycles and compute transitivity metrics.