{
  "abstract": "We present a novel viewpoint for uncertainty quantification. Uncertainty measures are not primitives, in need of axioms and argumentation, but instead consequences, of higher-level modelling decisions. We show how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss. Reverse cross entropy provides a prominent example, where decomposition recovers the classic information-theoretic uncertainty terms. The same approach recovers numerous measures previously proposed across the UQ literature, providing them a common theoretical foundation. This suggests a new approach to UQ: given a modelling scenario and strictly proper loss, the corresponding epistemic and aleatoric terms are induced by the subjective-risk decomposition. We then extend our view to learning theory: we introduce and analyse subjective risk analogues of excess risk, approximation error and estimation error, and identify the connections to UQ. We consider this a first step towards a full learning-theoretic framework for uncertainty quantification.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.15196",
  "author_total": 3,
  "authors": [
    {
      "name": "Raghad Alamri"
    },
    {
      "name": "Michele Caprio"
    },
    {
      "name": "Gavin Brown"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5bxe411s5r4ts.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.15196",
  "fulltext_url": null,
  "id": "5bxe411s5r4ts",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.15196",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Gaussian Processes and Bayesian Inference"
    ]
  ],
  "openalex": "W7169566644",
  "pdf_url": "https://arxiv.org/pdf/2607.15196",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5bxe411s5r4ts.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:10Z",
      "native_id": "2607.15196",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.15196"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Subjective Risk Decomposition: A New View for Uncertainty Quantification",
  "title_text": "Subjective Risk Decomposition: A New View for Uncertainty Quantification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:10Z",
  "url": "https://research.draconitic.com/paper/5bxe411s5r4ts",
  "venue": "arXiv",
  "versions": []
}