{
  "ai.module": "information_theory",
  "ai.version": "1.0",
  "ai.purpose": "Provide a regime-aware, distinction-first interface to Information Theory as a 0D-style coherence grammar.",
  "ai.keywords": [
    "information",
    "entropy",
    "mutual information",
    "codes",
    "channels",
    "distinctions",
    "constraints",
    "triadic frameworks"
  ],

  "identity": {
    "name": "Information Theory",
    "category": "Scientific Theory",
    "summary": "A distinction-first coherence grammar describing entropy, codes, channels, and constraints on communication and encoding.",
    "regime": ["R0", "R1", "R2→R3"],
    "status": "canon-ready"
  },

  "lineage": {
    "originators": ["Claude Shannon", "Andrey Kolmogorov", "Norbert Wiener"],
    "historical_period": "20th Century",
    "source_domain": "Mathematics and Communication Theory",
    "related_theories": [
      "thermodynamics",
      "computation",
      "evolutionary_biology",
      "quantum_mechanics"
    ],
    "notes": "Information Theory describes distinctions under constraints. Entropy, codes, and channels are operators, not physical substances."
  },

  "operators": {
    "primary": [
      "entropy",
      "mutual_information",
      "channel_capacity",
      "encoding",
      "decoding"
    ],
    "secondary": [
      "noise",
      "redundancy",
      "compression",
      "error_correction",
      "signal_alphabet"
    ],
    "description": "Operators describe how distinctions are created, preserved, transmitted, and constrained across channels and substrates."
  },

  "drift": {
    "risks": [
      "treating information as a physical substance",
      "assuming bits are ontological",
      "overextending Shannon entropy into unrelated domains",
      "confusing encoding with meaning"
    ],
    "boundaries": [
      "information is distinctions under constraints",
      "entropy measures uncertainty, not value",
      "channels impose structure, not semantics",
      "codes operate on alphabets, not metaphysics"
    ]
  },

  "coherence": {
    "invariants": [
      "entropy reduction requires constraints",
      "mutual information reflects shared distinctions",
      "channel capacity limits transmission",
      "error correction requires redundancy"
    ],
    "failure_modes": [
      "noise overwhelming signal",
      "loss of distinctions",
      "channel collapse",
      "overcompression"
    ]
  },

  "cross_module": {
    "supports": [
      "computation",
      "evolutionary_biology",
      "thermodynamics",
      "quantum_mechanics"
    ],
    "supported_by": [
      "probability_theory",
      "regime_awareness"
    ],
    "integration_notes": "Information Theory integrates cleanly with RTT engines as a 0D-style grammar for distinctions, constraints, and uncertainty."
  }
}