{
  "abstract": "Service counts are often converted into capacity or inventory decisions with independent Poisson models, although clustering and latent heterogeneity can make the counts substantially more variable. We present reinforced-count simulation (RCS) as a low-parameter, pre-deployment stress test: it asks whether a decision calibrated under independence remains adequate when type shares persist. RCS is compared with independent Poisson, negative-binomial, empirical-residual and Scarf moment-robust decisions. The empirical analysis uses two public datasets. RAND Health Insurance Experiment physician-visit counts provide a cross-sectional held-out test (20,190 observations), and five categories of New York City 311 requests provide an external temporal test (1,096 days and 26 rolling origins). In the RAND analysis at a lost-event-to-holding-cost ratio of 20, over-dispersion-aware decisions reduced held-out cost relative to Poisson by 15.0% for RCS, 15.6% for negative binomial and 17.3% for the empirical quantile; fill rate increased from 76.5% to 88.2%–91.1%. In the NYC analysis at a ratio of 10, the RCS mean paired cost improvement was 18.7% (95% confidence interval, 12.4%–25.0%) and fill rate increased from 93.3% to 96.8%. A multi-type transfer experiment showed that each calibrated model was best in its matching environment; using a mismatched model produced 6.0%–20.0% regret. Joint sensitivity analysis, concentration-parameter learning curves and cost-ratio perturbations identify when the diagnostic is useful and when parameter error can dominate model choice. RCS is a reproducible check on mean-based decisions when composition dependence is uncertain, not a general demand model.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Public Basic Courses, Nanjing University of Industry Technology, Nanjing, China",
    "Office of Academic Affairs, Nanjing Medical University, Nanjing, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
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        0
      ],
      "name": "Saisai Hou"
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    {
      "affs": [
        1
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      "name": "Yunzhi Zhu"
    },
    {
      "name": "Sen Zhang"
    },
    {
      "name": "Ying Chen"
    }
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  "bibtex": "https://research.draconitic.com/paper/42ch2rkgx2cxa.bib",
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  "doi": "10.1371/journal.pone.0358767",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
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      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Monte Carlo method"
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      "plos",
      "/Research and analysis methods/Simulation and modeling"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358767&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/42ch2rkgx2cxa.ris",
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    {
      "first_seen": "2026-10-30T12:17:49Z",
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      "url": "https://doi.org/10.1371/journal.pone.0358767"
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  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
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  "title": "Reinforced-count simulation for decision calibration under over-dispersed multi-type service demand",
  "title_text": "Reinforced-count simulation for decision calibration under over-dispersed multi-type service demand",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/42ch2rkgx2cxa",
  "venue": "PLOS One",
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}