{
  "handle": "ExplosiveNoise",
  "gloss": "What a single-model system filters out as noise can be signal belonging to another, overlapping model",
  "mechanism": "A system that values information only for {{shape_control}} labels as noise anything that would destabilise its current shape: the neighbouring conversation in a pub, an advert for cheap oranges on the way to buy milk, a gene arriving from another species. Much of this is not random; it is information for a different model of what the agent could be doing. A {{mono_modularity}} system can only ignore it. A {{meta_modularity}} system can switch to the model for which that information is signal, joining the conversation, diverting to the orange shop, incorporating the gene through horizontal gene transfer, gaining {{information_growth}} at the cost of changing shape. Switching models has a cost, so not every such signal is worth exploiting.",
  "invariants": [
    "Whether information is noise is relative to the model receiving it",
    "Exploiting such information requires an alternative model to switch to",
    "Switching models costs something and must be weighed against the value of the information"
  ],
  "failure_modes": [
    "Filtering out every input that does not serve the current shape",
    "Switching models for every distraction, so no shape is held long enough to gain from it",
    "Assuming that information which looks random to one model is random"
  ],
  "_meta": {
    "path": [
      "Mind",
      "Inference"
    ],
    "ring": 2,
    "tier": 2,
    "source": "Adam Timlett, On the Origin of Risk (2025), Ch 4 p.44-45"
  },
  "sema_id": "sema:ExplosiveNoise#mh:SHA-256:fde13e8fe8e40d0be4bd3e133570f284ff8d1dc5edab8dde4b029c09eb96e8d3",
  "sema_ref": "ExplosiveNoise#fde1",
  "sema_stub": "fde1",
  "dependencies": {
    "references": {
      "information_growth": "sema:InformationGrowth#mh:SHA-256:9a86182b8561d0eada5a28b0ceb30fa00500e725d050f760805b8f0da37125de",
      "meta_modularity": "sema:MetaModularity#mh:SHA-256:cb6cc554a6eb00214b5e2fba0504373bc4312dd5d74ec4fdc71bb20224197aef",
      "mono_modularity": "sema:MonoModularity#mh:SHA-256:944c5f3013c718b6999c18f4628b584633ba3a8e294a6c268e0c5e2bfcf1d22e",
      "shape_control": "sema:ShapeControl#mh:SHA-256:185f506a48d483fc549fb77ece35dc1f3c54180a0c31657960a2fa6daf155a2d"
    }
  },
  "sema_layer": "Mind",
  "sema_category": "Inference",
  "used_by": {}
}