{
  "handle": "InformationDividend",
  "gloss": "The payoff from changing shape to exploit reliable short-term information, which exists even when the long-range forecast is entirely correct",
  "mechanism": "A long-range prediction (80% sun over ten days, total rainfall for July, the resources a project needs) can be entirely correct and still leave value unrealised, because it does not say which days will be sunny. An agent that holds one shape for the whole period, such as one policy for the year, gets only the payoff of the average. An agent that changes shape day by day on reliable one-day forecasts, staying home on rain days and going to the park on sun days, collects an extra payoff from the variation inside the forecast. This dividend from {{information_growth}} requires three things: transition costs low enough to make changing worthwhile, short-term information reliable enough not to be noise, and local conditions that differ from the average. It does not depend on uncertainty: it arises even when the world is predictable, so it is distinct from the case for agility based on bad planning. Holding {{shape_control}} over a long period forgoes it.",
  "invariants": [
    "The dividend can exist without any error in the long-range prediction",
    "It is bounded by the gap between the value of changing shape and the cost of changing it",
    "It disappears if short-term information is unreliable or local conditions match the average"
  ],
  "preconditions": [
    "Changing shape costs less than the value it unlocks",
    "Short-term information is reliable"
  ],
  "failure_modes": [
    "Setting one blanket policy from summary statistics and forgoing the variation inside them",
    "Changing shape on unreliable short-term signals, which lowers the payoff (add inertia instead)",
    "Justifying flexibility only by uncertainty, so better forecasts are wrongly taken to remove the need for it"
  ],
  "_meta": {
    "path": [
      "Mind",
      "Strategy"
    ],
    "ring": 2,
    "tier": 2,
    "source": "Adam Timlett, On the Origin of Risk (2025), Ch 4 p.45-51, Ch 6 p.58-60, Ch 7 p.62"
  },
  "sema_id": "sema:InformationDividend#mh:SHA-256:80a31b65e2e233ba0230d8a6477e43b1d8308ae8a020e5135f1665602ec79280",
  "sema_ref": "InformationDividend#80a3",
  "sema_stub": "80a3",
  "dependencies": {
    "references": {
      "information_growth": "sema:InformationGrowth#mh:SHA-256:9a86182b8561d0eada5a28b0ceb30fa00500e725d050f760805b8f0da37125de",
      "shape_control": "sema:ShapeControl#mh:SHA-256:185f506a48d483fc549fb77ece35dc1f3c54180a0c31657960a2fa6daf155a2d"
    }
  },
  "sema_layer": "Mind",
  "sema_category": "Strategy",
  "used_by": {
    "references": {
      "BalancedScopeChange": "sema:BalancedScopeChange#mh:SHA-256:94e1ba81b233cf8461371d7777d0c4342aa94a04628ee421915460b9c470b4a0",
      "FractalityTest": "sema:FractalityTest#mh:SHA-256:54821a508ea02eee285d10a669d27ed3a6179e5d6e3169c25faf14cba63ec323",
      "ResourceShapeSeparation": "sema:ResourceShapeSeparation#mh:SHA-256:a134b6a1aba900011db79661ee8a6c3cb9a4cc1e0da8ff78e1286afc9954a657",
      "StrategicIntermediateState": "sema:StrategicIntermediateState#mh:SHA-256:b8ce9d180be004cb16f1fdaee47810d11a4d33fb94c7a831befc5a2e1df04a62",
      "WeatherAgent": "sema:WeatherAgent#mh:SHA-256:25e1d749c629abfedc88613c936da6d6aa40e661d411d6764fe70b30ae690274"
    }
  }
}