Before any domain-specific scoring can happen, the behavioral AI platform needs to characterise the raw behavioral signal. The foundational engines do this — measuring unpredictability, information density, spatial structure, and adaptive feedback.

The Engines

  • Behavioral Entropy Engine — Applies Shannon entropy to behavioral sequences. High entropy = unpredictable actor. Low entropy = highly patterned behavior. Used as a baseline modifier by many downstream engines.
  • Information Theory Engine — Measures the mutual information between behavioral signals. Identifies which signals carry the most predictive information about each other.
  • Geometric/Topological Engine — Maps behavioral patterns into a state space and uses topological data analysis (TDA) to find persistent structures in the data.
  • Meta-Learning Engine — Monitors the accuracy of other engines over time and adjusts their weights in collation. If the Bayesian engine has been underperforming for a specific context, meta-learning reduces its contribution.

Code Walkthrough

// Behavioral entropy engine (simplified)
function scoreBehavioralEntropy(signals) {
  const freq = computeFrequencyDistribution(signals.sequence);
  const entropy = -Object.values(freq)
    .reduce((sum, p) => sum + (p > 0 ? p * Math.log2(p) : 0), 0);

  // Normalize to 0–1 scale relative to maximum possible entropy
  const maxEntropy = Math.log2(Object.keys(freq).length);
  return {
    score:      maxEntropy > 0 ? entropy / maxEntropy : 0,
    label:      entropy > 0.8 ? "high-variability" : "structured-pattern",
    confidence: signals.sequence.length >= 10 ? 0.9 : 0.5,
  };
}

What to Watch For

  • Entropy scores are meaningless with fewer than 10 behavioral observations. The confidence propagation system handles this with the low_evidence reducer.
  • The meta-learning engine needs at least 30 days of ground truth feedback before its weights are reliable. Run it in research-only mode for the first month.