{
  "annotation_id": "6d8a1465-e848-423a-b4dc-7a50c6e83255",
  "date_created": "2026-03-02T18:01:07.839000Z",
  "date_modified": "2026-03-02T18:01:07.839000Z",
  "file_hash": "b8caa5194d59a3b469cd255b99888c7e552f95f1881b5ce1d1d3aeceb7199393",
  "private": false,
  "record": {
    "abstract": "We present a memory-based snowdrift game (MBSG) taking place on networks. We\nfound that, when a lattice is taken to be the underlying structure, the\ntransition of spatial patterns at some critical values of the payoff parameter\nis observable for both 4 and 8-neighbor lattices. The transition points as well\nas the styles of spatial patterns can be explained by local stability analysis.\nIn sharp contrast to previously reported results, cooperation is promoted by\nthe spatial structure in the MBSG. Interestingly, we found that the frequency\nof cooperation of the MBSG on a scale-free network peaks at a specific value of\nthe payoff parameter. This phenomenon indicates that properly encouraging\nselfish behaviors can optimally enhance the cooperation. The memory effects of\nindividuals are discussed in detail and some non-monotonous phenomena are\nobserved on both lattices and scale-free networks. Our work may shed some new\nlight on the study of evolutionary games over networks.",
    "arxiv_id": "physics/0604103",
    "authors": [
      "Wen-Xu Wang",
      "Jie Ren",
      "Guanrong Chen",
      "Bing-Hong Wang"
    ],
    "categories": [
      "physics.soc-ph"
    ],
    "doi": "10.1103/PhysRevE.74.056113",
    "journal_ref": "Phys. Rev. E 74, 056113 (2006)",
    "title": "Memory-Based Snowdrift Game on Networks",
    "url": "https://arxiv.org/abs/physics/0604103"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "3c5b29b0-c4d4-43c8-8341-c2dce687dfa9",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}