{
  "handle": "FractionalMetaModel",
  "gloss": "An implementation that is at once one working model and a partial implementation of several others, so that some functions can be completed or removed with a small change",
  "mechanism": "Most implementations with more than one component sit halfway between models. Some functions are complete; others are partly built: a memorable password that fails one strength rule, a data column brought through two of three pipeline stages, a call I thought about making but did not. These partly built functions are {{proximate_options}}: if 6 of 7 components are in place, changing 1 completes the function. Unlike a hierarchical meta-model one level up, which loses domain information, a fractional meta-model sits at the same level as the solution and keeps it. The partial functions show up as friction, a raised {{computational_entropy_rate}}. When an {{exogenous_stress}} arrives that needs one of them, little work is needed to complete it; if the stressor never comes, the partial function can be removed. Organisations whose members hold different views of what to do are fractional meta-models of this kind, and the same structure gives {{meta_modularity}} a concrete mechanism.",
  "invariants": [
    "The model is at the same level as the solution, not a level of abstraction above it",
    "Completing a partly built function needs only the missing components",
    "Partly built functions appear as friction in the system's channels"
  ],
  "failure_modes": [
    "Defining meta-models only as data hierarchies, which lose the domain",
    "Streamlining away every partly built function as waste",
    "Leaving partly built functions that will never be needed, at a standing cost"
  ],
  "_meta": {
    "path": [
      "Mind",
      "Strategy"
    ],
    "ring": 2,
    "tier": 2,
    "source": "Adam Timlett, On the Origin of Risk (2025), Technical Appendix I p.145-151, Appendix II p.155"
  },
  "sema_id": "sema:FractionalMetaModel#mh:SHA-256:b25a96009ba8797d2c4a8673cebfe38ebb0326d237a3669a2a2d7f56aeb5e2c2",
  "sema_ref": "FractionalMetaModel#b25a",
  "sema_stub": "b25a",
  "dependencies": {
    "references": {
      "computational_entropy_rate": "sema:ComputationalEntropyRate#mh:SHA-256:4f452b8ec7cb60a7e69fa5509ed17eaa33a77019be8631a7bbb71ae1a24c9144",
      "exogenous_stress": "sema:ExogenousStress#mh:SHA-256:6a33d65f1361a95ad72b8a8d59c90bf253e08f804e2a79532977d5113987b672",
      "meta_modularity": "sema:MetaModularity#mh:SHA-256:cb6cc554a6eb00214b5e2fba0504373bc4312dd5d74ec4fdc71bb20224197aef",
      "proximate_options": "sema:ProximateOptions#mh:SHA-256:9b4d9572f268a6bfba63e1edf49f10434b15e7f0fed79e57e815d689317b0ddb"
    }
  },
  "sema_layer": "Mind",
  "sema_category": "Strategy",
  "used_by": {
    "references": {
      "EndogenousStress": "sema:EndogenousStress#mh:SHA-256:5367f1ad026491329822553c265a3e9fefb5555dc7779d8bb5f772ce6af86aa2",
      "FractionalDelivery": "sema:FractionalDelivery#mh:SHA-256:fe1c10ac39652798ee9b2edd66406f9b1857f125b220f962d90fe9934cdccca7",
      "StoppingRule": "sema:StoppingRule#mh:SHA-256:9a1d4616af79fa4b654e36ec26704887ff8f8ac246c1bf71be5207948aa863fc"
    }
  }
}