Individuals do not behave in isolation. They are embedded in institutions, constrained by physical and social reality, and capable of learning from feedback. The adaptive human systems engines model all three dimensions.

The Engines

  • Institutional Engine — Models organizational behavior: how institutions constrain, enable, and shape individual behavioral patterns. A lawyer behaving within a law firm operates under different constraints than the same person acting independently.
  • Reality Constraint Engine — Models the physical and social constraints that bound possible behavior: time, geography, resource availability, social norms.
  • Recursive Improvement Engine — Implements a controlled self-improvement loop: the engine monitors its own outputs, identifies systematic errors, and proposes model updates for human review before deployment.

Code Walkthrough

// Recursive improvement: propose a model update
function proposeModelUpdate(engineId, errorAnalysis) {
  const proposal = {
    engineId,
    timestamp:      new Date().toISOString(),
    errorAnalysis,
    proposedDelta:  computeModelDelta(errorAnalysis),
    confidence:     errorAnalysis.sampleSize > 1000 ? 0.85 : 0.60,
    requiresReview: true,  // always: recursive improvement never auto-deploys
  };

  humanReviewQueue.push(proposal);
  auditLogger.logProposal(proposal);
  return proposal;
}

What to Watch For

  • Recursive improvement must never auto-deploy. All proposed updates require human review and a full test suite pass before promotion.
  • Institutional constraints change. The institutional engine must be updated when the institutional context changes — it is not a set-and-forget component.