Understanding what an actor wants at the motivation level — security, belonging, esteem, or self-actualisation — is more predictive of behavior than understanding what they say they want. The motivation hierarchy engine makes this computable from behavioral signals.

The Engines

  • Tier Mapping — Five tiers adapted from Maslow: security (T1), belonging (T2), recognition (T3), achievement (T4), purpose (T5). Each tier has a set of behavioral indicators.
  • Dominant Motivation Score — The tier with the highest evidence-weighted score is the dominant motivation. The score is a probability distribution across tiers, not a single label.
  • Motivation Shift Detection — Detects when an actor transitions between dominant tiers. A sales prospect moving from T3 (recognition) to T1 (security) in week 3 of a deal is a strong signal of risk entering the process.

Code Walkthrough

// Score motivation tier distribution
function scoreMotivationHierarchy(signals) {
  const tierScores = {
    security:    scoreTier(signals, SECURITY_INDICATORS),
    belonging:   scoreTier(signals, BELONGING_INDICATORS),
    recognition: scoreTier(signals, RECOGNITION_INDICATORS),
    achievement: scoreTier(signals, ACHIEVEMENT_INDICATORS),
    purpose:     scoreTier(signals, PURPOSE_INDICATORS),
  };

  const dominant = Object.entries(tierScores)
    .sort(([, a], [, b]) => b - a)[0][0];

  return {
    distribution: tierScores,
    dominant,
    label: `${dominant}-motivation-indicators-present`,
  };
}

What to Watch For

  • Never collapse the tier distribution to a single label in high-stakes contexts. The full distribution carries more information.
  • Motivation tier data is highly sensitive. Treat it as special category data under GDPR and restrict access to the minimum necessary personnel.