{
  "annotation_id": "f1f75f1d-ddb9-4c0b-83fe-9f9a2a59ed22",
  "date_created": "2026-03-02T18:01:17.177000Z",
  "date_modified": "2026-03-02T18:01:17.177000Z",
  "file_hash": "074e0e9a71e8424e9d5bc72c2cb49e2a59c0c30f563e4fa8383c00a79c3c5188",
  "private": false,
  "record": {
    "abstract": "The agent-based model of stock price dynamics on a directed evolving complex\nnetwork is suggested and studied by direct simulation. The stationary regime is\nmaintained as a result of the balance between the extremal dynamics, adaptivity\nof strategic variables and reconnection rules. The inherent structure of node\nagent \"brain\" is modeled by a recursive neural network with local and global\ninputs and feedback connections. For specific parametric combination the\ncomplex network displays small-world phenomenon combined with scale-free\nbehavior. The identification of a local leader (network hub, agent whose\nstrategies are frequently adapted by its neighbors) is carried out by repeated\nrandom walk process through network. The simulations show empirically relevant\ndynamics of price returns and volatility clustering. The additional emerging\naspects of stylized market statistics are Zipfian distributions of fitness.",
    "arxiv_id": "physics/0701156",
    "authors": [
      "D. Horvath",
      "Z. Kuscsik"
    ],
    "categories": [
      "physics.soc-ph",
      "q-fin.ST"
    ],
    "doi": "10.1142/S0129183107011388",
    "title": "Structurally dynamic spin market networks",
    "url": "https://arxiv.org/abs/physics/0701156"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "7594bc47-e948-488a-b8a3-763a91ad306a",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}