{
  "abstract": "FINDING: Causal structure discovery is fundamentally constrained by algorithmic information loss; permutation-based methods and Kolmogorov complexity provide the measure-theoretic backbone for separating signal from noise in causal graphs. | MATH: Kolmogorov complexity \\(K(x) = \\min_p \\{|p| : U(p)=x\\}\\); algorithmic mutual information \\(I_A(x;y) = K(x) + K(y) - K(x,y)\\); causal Markov condition \\(P(v_1,\\dots,v_n) = \\prod_i P(v_i \\mid \\text{pa}(v_i))\\); integrated information \\(\\Phi\\) (Tononi) — lossless variant requires \\(K(\\Phi) \\approx K(\\text{system})\\) (no compression below intrinsic complexity); permutation search space size \\(n!\\) over DAGs, with intervention targets unknown → complexity grows as \\(O(n! \\cdot 2^m)\\) for \\(m\\) latent targets. | CONNECTION: The golden ratio appears implicitly in the trade-off between algorithmic compression and causal fidelity — the optimal partition of information into \"mechanism\" vs \"noise\" follows a fixed-point where \\(K(\\text{model}) / K(\\text{\n\nAuthor: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher, United Kingdom"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Andrew Stewart Caldin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c7j69kd9s0bgm.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23635637",
  "fulltext_url": null,
  "id": "c7j69kd9s0bgm",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23635637",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c7j69kd9s0bgm.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:21:52Z",
      "native_id": "23635637",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23635637"
    }
  ],
  "subjects": [],
  "title": "Algorithmic Information Bounds for Causal Structure Discovery — E8 Intelligence Research",
  "title_text": "Algorithmic Information Bounds for Causal Structure Discovery — E8 Intelligence Research",
  "type": "preprint",
  "updated_at": "2026-11-08T12:21:52Z",
  "url": "https://research.draconitic.com/paper/c7j69kd9s0bgm",
  "venue": "Zenodo",
  "versions": []
}