{
  "ai.module": "chaos_theory",
  "ai.version": "1.0",
  "ai.purpose": "Provide a regime-aware, operator-level interface to Chaos Theory as a diagnostic lens for nonlinear behavior.",
  "ai.keywords": [
    "chaos",
    "nonlinear systems",
    "sensitivity to initial conditions",
    "divergence",
    "attractors",
    "recurrence",
    "regime awareness",
    "triadic frameworks"
  ],

  "identity": {
    "name": "Chaos Theory",
    "category": "Scientific Theory",
    "summary": "A behavioral description of nonlinear systems exhibiting sensitivity to initial conditions, divergence, and complex temporal patterns.",
    "regime": ["R3", "R2→R3", "R1"],
    "status": "canon-ready"
  },

  "lineage": {
    "originators": ["Edward Lorenz", "Henri Poincaré"],
    "historical_period": "20th Century",
    "source_domain": "Nonlinear Dynamics",
    "related_theories": [
      "information_theory",
      "thermodynamics",
      "quantum_mechanics"
    ],
    "notes": "Chaos Theory describes behavior, not substrate. It is a signal of insufficient resolution or incomplete lineage tracking."
  },

  "operators": {
    "primary": [
      "divergence",
      "recurrence",
      "attractor",
      "sensitivity",
      "nonlinearity"
    ],
    "secondary": [
      "finite_precision",
      "projection_loss",
      "dimensional_compression"
    ],
    "description": "Operators describe observable behaviors of nonlinear systems under constrained resolution or incomplete lineage."
  },

  "drift": {
    "risks": [
      "treating chaos as ontology",
      "assuming unpredictability is fundamental",
      "overextending low-dimensional projections"
    ],
    "boundaries": [
      "chaos is a diagnostic regime",
      "chaos does not describe substrate",
      "chaos emerges from insufficient resolution"
    ]
  },

  "coherence": {
    "invariants": [
      "sensitivity increases as resolution decreases",
      "divergence is bounded by system constraints",
      "attractors represent stable recurrence patterns"
    ],
    "failure_modes": [
      "misinterpreting noise as chaos",
      "ignoring lineage effects",
      "assuming randomness where structure exists"
    ]
  },

  "cross_module": {
    "supports": [
      "information_theory",
      "thermodynamics",
      "quantum_mechanics"
    ],
    "supported_by": [
      "dimensional_analysis",
      "regime_awareness"
    ],
    "integration_notes": "Chaos Theory integrates cleanly with RTT engines as a behavioral diagnostic for nonlinear regime transitions."
  }
}