{
  "abstract": "Stochastic processes underpin dynamics across biology, physics, epidemiology, and finance, yet accurately simulating them remains a major challenge. Classical approaches such as the Gillespie algorithm are exact for Markovian, time-independent systems, where propensities depend only on the current state and agents of a given type are statistically identical. While efficient, this framework misses a defining feature of many real systems: heterogeneity and memory at the level of individual agents. Cells may divide or differentiate on distinct intrinsic timescales, individuals may preferentially interact with specific partners, and inter-event-time distributions can deviate strongly from the exponential. We introduce MOSAIC (Modeling of Stochastic Agents with Individual Complexity), a general and scalable framework that embeds agent-specific properties directly into the dynamics. MOSAIC unifies heterogeneous rates, dynamic interaction preferences, and both Markovian and non-Markovian waiting-time distributions within a single stochastic formalism, while retaining Gillespie-like computational cost.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Nature Communications, 2026",
  "arxiv": "2609.31125",
  "author_total": 5,
  "authors": [
    {
      "name": "Aurelien Pelissier"
    },
    {
      "name": "Miroslav Phan"
    },
    {
      "name": "Didier Le Bail"
    },
    {
      "name": "Niko Beerenwinkel"
    },
    {
      "name": "Maria Rodriguez Martinez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1p3jpffm1k2py.bib",
  "cited_by": 3,
  "doi": "10.1038/s41467-026-69817-y",
  "fulltext_url": null,
  "id": "1p3jpffm1k2py",
  "issns": [
    "2041-1723",
    "2041-1723"
  ],
  "landing_url": "https://hal.science/hal-05763067v1",
  "language": "en",
  "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": "CC0-1.0",
  "native_subjects": [
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      "math-ph"
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  "openalex": "W7133218308",
  "pdf_url": "https://hal.science/hal-05763067/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/1p3jpffm1k2py.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:40Z",
      "native_id": "hal-05763067",
      "source": "hal",
      "url": "https://hal.science/hal-05763067v1"
    },
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2609.31125",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.31125"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math-ph",
      "subfield": "mathematical-physics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.soc-ph",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Unifying non-Markovian Dynamics and Agent Heterogeneity in Scalable Stochastic Networks",
  "title_text": "Unifying non-Markovian Dynamics and Agent Heterogeneity in Scalable Stochastic Networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:24:57Z",
  "url": "https://research.draconitic.com/paper/1p3jpffm1k2py",
  "venue": "Nature Communications",
  "versions": []
}