{
  "annotation_id": "7e74121f-c754-4671-afda-7a93b747dd4d",
  "date_created": "2026-03-02T18:01:07.542000Z",
  "date_modified": "2026-03-02T18:01:07.542000Z",
  "file_hash": "bdb2030aef2e5287da4ce70179fae6d7a3882f4cf9c15525509fb363a30bab43",
  "private": false,
  "record": {
    "abstract": "We discuss the feasibility of predicting, managing and subsequently\nmanipulating, the future evolution of a Complex Adaptive System. Our archetypal\nsystem mimics a population of adaptive, interacting objects, such as those\narising in the domains of human health and biology (e.g. cells), financial\nmarkets (e.g. traders), and mechanical systems (e.g. robots). We show that\nshort-term prediction yields corridors along which the model system will, with\nhigh probability, evolve. We show how the widths and average direction of these\ncorridors varies in time as the system passes through regions, or pockets, of\nenhanced predictability and/or risk. We then show how small amounts of\n\u0027population engineering\u0027 can be undertaken in order to steer the system away\nfrom any undesired regimes which have been predicted. Despite the system\u0027s many\ndegrees of freedom and inherent stochasticity, this dynamical, \u0027soft\u0027 control\nover future risk requires only minimal knowledge about the underlying\ncomposition of the constituent multi-agent population.",
    "arxiv_id": "physics/0605065",
    "authors": [
      "David M. D. Smith",
      "Neil F. Johnson"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "title": "Predictability, Risk and Online Management in a Complex System of   Adaptive Agents",
    "url": "https://arxiv.org/abs/physics/0605065"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b078311d-391b-4ce7-b0b3-57327fff3efd",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}