{
  "handle": "MetaModularity",
  "gloss": "A body plan that holds many overlapping but non-identical models of the same problem, ready to instantiate whichever fits",
  "mechanism": "Instead of one model or a set of non-overlapping standard parts, the system carries a variety of models that solve similar and overlapping problems in different ways: a species' population of genetically varied individuals, a plant that switches genes on and off to acclimatise, E. coli's many metabolic pathways able to live on dozens of different energy sources, Feynman's 'Babylonian' habit of keeping every alternative formulation of a theory in mind. When conditions change, some subset of models already fits; when one assumption fails, the others still stand. As mass is added (a population grows), the absolute variety of models grows with it, so information grows with mass. Meta-modularity is measured by the variety of information a system can process and the variety of valuable forms it can produce in a domain.",
  "invariants": [
    "Models overlap in what they address but differ in how they address it",
    "Variety of models grows as the system grows",
    "The value lies in overlapping, non-identical information, not in identical spares"
  ],
  "preconditions": [
    "The environment varies enough that no single model is best everywhere"
  ],
  "failure_modes": [
    "Reducing it to plain redundancy (identical backups), which only protects against loss and adds no options",
    "Calling a data hierarchy a meta-model even though it holds no information about the domain",
    "Assimilating all models into one standard, so variety is lost"
  ],
  "_meta": {
    "path": [
      "Mind",
      "Strategy"
    ],
    "ring": 2,
    "tier": 2,
    "source": "Adam Timlett, On the Origin of Risk (2025), Ch 1 p.19-25, Ch 2 p.28-32"
  },
  "sema_id": "sema:MetaModularity#mh:SHA-256:cb6cc554a6eb00214b5e2fba0504373bc4312dd5d74ec4fdc71bb20224197aef",
  "sema_ref": "MetaModularity#cb6c",
  "sema_stub": "cb6c",
  "sema_layer": "Mind",
  "sema_category": "Strategy",
  "used_by": {
    "references": {
      "AnthropomorphicRiskBias": "sema:AnthropomorphicRiskBias#mh:SHA-256:abbe331f94cedf8bf3306ff3e638db4821bb4b624a3c35cccee6b2cf9e5bfe9b",
      "ChimericalComputation": "sema:ChimericalComputation#mh:SHA-256:b99db7f13c7095cec1c08d7d94cb41755cbcb53789767fe03252a88ce9d7417a",
      "CommunicativeRedundancy": "sema:CommunicativeRedundancy#mh:SHA-256:dcebae06ba3e348b7fa1aae8be8b7e87151d48b28ac90efc152c95e883770319",
      "CorrectAsYouGrow": "sema:CorrectAsYouGrow#mh:SHA-256:006233f72b4faf4e294db05f8741b831d591db22f82e3e04282c4b7bce250742",
      "ExplosiveNoise": "sema:ExplosiveNoise#mh:SHA-256:fde13e8fe8e40d0be4bd3e133570f284ff8d1dc5edab8dde4b029c09eb96e8d3",
      "FractionalMetaModel": "sema:FractionalMetaModel#mh:SHA-256:b25a96009ba8797d2c4a8673cebfe38ebb0326d237a3669a2a2d7f56aeb5e2c2",
      "MassAmplifiedError": "sema:MassAmplifiedError#mh:SHA-256:19b274047a79ec641a4d9db1074d82de3def8c245515a6d57ba7b7df605d6c70",
      "NaturalRiskMathematics": "sema:NaturalRiskMathematics#mh:SHA-256:ed8f4d1b793c26c936bc373abda92f57352271c6a4cc268a3e7ac6a7c96f4d06",
      "OrganicWork": "sema:OrganicWork#mh:SHA-256:3df69c75934f1e354c6a414cbf27652ad76389f9605b7059c57a176c93337cbc",
      "SelfCheckingBias": "sema:SelfCheckingBias#mh:SHA-256:a395d033d3f7b496343149133cfd76ffa831aacf8c2ce455e42c5fc54cc714b6",
      "ShapeLossAsGrowth": "sema:ShapeLossAsGrowth#mh:SHA-256:e110c719cd96e48f7580d5438f7aa093c035ea39ab7cd27d6d6f4ace400e4bbd"
    }
  }
}