@vote-internals/logic

Mathematical and statistical calculation engine for ranked ballot voting systems. Implements 3-2-1 weighted Borda scoring, paired covariance and variance estimators, Z-score hypothesis testing for rank separation, Empirical Bayesian shrinkage for exposure regularization, and multi-factor rank skew anomaly detection.

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Installation

Add the dependency to package.json:

{
  "dependencies": {
    "@vote-internals/logic": "github:SeraphInteractive/MCS-internal-logic#main"
  }
}

Or install via npm:

npm install github:SeraphInteractive/MCS-internal-logic

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API Reference & Usage

import {
  validate_ballot,
  aggregate_scores,
  evaluate_rank_separation,
  calculate_bayesian_shrinkage,
  analyze_raid_risk,
  type Ballot,
} from '@vote-internals/logic';

// 1. Ballot validation (3-2-1 allocation, uniqueness, completeness)
const ballot: Ballot = {
  voterId: 'user_123',
  rank1: 'entry_a', // 3 points
  rank2: 'entry_b', // 2 points
  rank3: 'entry_c', // 1 point
};
const validation = validate_ballot(ballot);

// 2. Score aggregation & 6N conservation verification
const { scores, isConserved, leaderboard } = aggregate_scores(
  ['entry_a', 'entry_b', 'entry_c'],
  [ballot]
);

// 3. Paired Z-score rank separation test
const separation = evaluate_rank_separation(
  scores.get('entry_a')!,
  scores.get('entry_b')!,
  [ballot]
);

// 4. Empirical Bayesian shrinkage
const shrunkScore = calculate_bayesian_shrinkage(
  scores.get('entry_a')!,
  20, // Total entries in universe
  100 // Total ballots cast
);

// 5. Rank skew anomaly analysis
const telemetry = analyze_raid_risk(scores.get('entry_a')!);

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Testing

Execute the test suite:

npm test