{
  "annotation_id": "be3b4379-28a1-439b-a175-be346c99943e",
  "date_created": "2026-03-02T18:01:14.588000Z",
  "date_modified": "2026-03-02T18:01:14.588000Z",
  "file_hash": "75878697323a65f05f021a3e170ddbcae37a02487ee3288845093ea01f7f3d88",
  "private": false,
  "record": {
    "abstract": "We study the effectiveness of recovery strategies for a dynamic model of\nfailure spreading in networks. These strategies control the distribution of\nresources based on information about the current network state and network\ntopology. In order to assess their success, we have performed a series of\nsimulation experiments. The considered parameters of these experiments are the\nnetwork topology, the response time delay and the overall disposition of\nresources. Our investigations are focused on the comparison of strategies for\ndifferent scenarios and the determination of the most appropriate strategy. The\nimportance of prompt response and the minimum sufficient quantity of resources\nare discussed as well.",
    "arxiv_id": "physics/0611244",
    "authors": [
      "Lubos Buzna",
      "Karsten Peters",
      "Hendrik Ammoser",
      "Christian Kuehnert",
      "Dirk Helbing"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "doi": "10.1103/PhysRevE.75.056107",
    "title": "Efficient Response to Cascading Disaster Spreading",
    "url": "https://arxiv.org/abs/physics/0611244"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f1209d78-6846-400e-ae68-f792f1a52a37",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}