{
  "annotation_id": "57629631-3340-479e-8fd3-5a9dfc9e9396",
  "date_created": "2026-03-02T18:01:04.182000Z",
  "date_modified": "2026-03-02T18:01:04.182000Z",
  "file_hash": "01001bc89313edb39009b1f54c450f50a4f22077a8a99cfc43dfba1eaf4d11d8",
  "private": false,
  "record": {
    "abstract": "The acquisition of competence is a key element in the ability to assert\noneself in the complex and rapidly changing modern worlds of work. This paper\nexamines the evolution of competence, i.e. the role of competences in an\nevolutionary problem-solving process, and the role of flexibility as a\nmeta-competence from the perspective of the general concept of\nGeometrically-Oriented Evolution THEory (G_O_E_THE).\n  We use evolutionary search agents as abstract models of real individuals and\ngroups. In a first approach the agents search for better competence profiles.\nIn a second approach they use competences to solve problems. A search agent\noperates in an abstract value landscape, a fitness landscape. The crucial\ndifference between the evolutionary search agents and other agent models is the\nfact that interaction patterns of the new agents incorporate evolutionary\nsearch strategies.\n  A special simulation tool called EVOLINO allows to simulate the hill-climbing\nprocess of search agents in complex landscapes.",
    "arxiv_id": "physics/0511232",
    "authors": [
      "Andrea Scharnhorst",
      "Werner Ebeling"
    ],
    "categories": [
      "physics.soc-ph",
      "physics.comp-ph"
    ],
    "title": "Evolutionary search agents in complex landscapes - a new model for the   role of competence and meta-competence (EVOLINO and other simulation tools)",
    "url": "https://arxiv.org/abs/physics/0511232"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "65c05089-2f0f-4b47-9741-99941c7682bf",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}