{
  "handle": "WeatherAgent",
  "gloss": "Choose between holding shape (adding inertia) and moving with the information (removing inertia) by weighing switching cost, information reliability and local variation",
  "mechanism": "An agent can be in one of several places (Home or Park) and the payoff of each depends on the state of the world (Rain or Sun); moving between them costs something. The same setup can be seen two ways at once, like a gestalt figure: as {{shape_control}} (a self-righting agent keeping its payoff maximal whatever the weather) or as exploiting information by changing shape. The decision rule follows from three variables. If short-term information is unreliable, add inertia: move less and wait for more evidence. If it is reliable, switching costs are low and the {{fractality_test}} shows local conditions differ from the average, remove inertia and move often to collect the {{information_dividend}}. If switching costs are high, hold one shape chosen from the long-range forecast. Lowering the cost of moving is itself a strategy for raising the dividend.",
  "invariants": [
    "Both views, maintaining shape and exploiting information by changing it, apply to the same agent",
    "More inertia is right for noisy information; less inertia for reliable, locally varying information",
    "The dividend from moving is capped by the cost of moving"
  ],
  "preconditions": [
    "The agent can change position or policy at a known cost",
    "Some short-range information about the current state is available"
  ],
  "failure_modes": [
    "Treating instability as noise when it is reliable information that averages out over the long range",
    "Moving on every signal when signals are unreliable",
    "Not investing in lowering switching costs when the dividend would justify it"
  ],
  "_meta": {
    "path": [
      "Mind",
      "Strategy"
    ],
    "ring": 2,
    "tier": 2,
    "source": "Adam Timlett, On the Origin of Risk (2025), Ch 4 p.44-51, Ch 6 p.56-60"
  },
  "sema_id": "sema:WeatherAgent#mh:SHA-256:25e1d749c629abfedc88613c936da6d6aa40e661d411d6764fe70b30ae690274",
  "sema_ref": "WeatherAgent#25e1",
  "sema_stub": "25e1",
  "dependencies": {
    "references": {
      "fractality_test": "sema:FractalityTest#mh:SHA-256:54821a508ea02eee285d10a669d27ed3a6179e5d6e3169c25faf14cba63ec323",
      "information_dividend": "sema:InformationDividend#mh:SHA-256:80a31b65e2e233ba0230d8a6477e43b1d8308ae8a020e5135f1665602ec79280",
      "shape_control": "sema:ShapeControl#mh:SHA-256:185f506a48d483fc549fb77ece35dc1f3c54180a0c31657960a2fa6daf155a2d"
    }
  },
  "sema_layer": "Mind",
  "sema_category": "Strategy",
  "used_by": {
    "references": {
      "VectorOptionTimeline": "sema:VectorOptionTimeline#mh:SHA-256:96ae792943d38f0ea59da99e62cd63e103841a75c3a2e6a056a6040646fe17a3"
    }
  }
}