{
  "abstract": "Regulatory approval of products in high-stakes domains such as drug development requires statistical evidence of safety and efficacy through large-scale randomized controlled trials. However, the high financial cost of these trials may deter developers who lack absolute certainty in their product's efficacy, ultimately stifling the development of `moonshot' products that could offer high social utility. To address this inefficiency, in this paper, we introduce a statistical protocol for experimentation where the product developer (the agent) conducts a randomized controlled trial sequentially and the regulator (the principal) partially subsidizes its cost. By modeling the protocol using a belief Markov decision process, we show that the agent's optimal strategy can be found efficiently using dynamic programming. Further, we show that the social utility is a piecewise linear and convex function over the subsidy level the principal selects, and thus the socially optimal subsidy can also be found efficiently using divide-and-conquer. Simulation experiments using publicly available data on antibiotic development and approval demonstrate that our statistical protocol can be used to increase social utility by more than $35$$\\%$ relative to standard, non-sequential protocols.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.06520",
  "author_total": 3,
  "authors": [
    {
      "name": "Ander Artola Velasco"
    },
    {
      "name": "Stratis Tsirtsis"
    },
    {
      "name": "Manuel Gomez-Rodriguez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3rxbs4xqg3fay.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.06520",
  "fulltext_url": null,
  "id": "3rxbs4xqg3fay",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.06520",
  "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.GT"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.MA"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods in Clinical Trials"
    ]
  ],
  "openalex": "W7160621034",
  "pdf_url": "https://arxiv.org/pdf/2605.06520",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3rxbs4xqg3fay.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:33:54Z",
      "native_id": "2605.06520",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.06520"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Optimizing Social Utility in Sequential Experiments",
  "title_text": "Optimizing Social Utility in Sequential Experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:54Z",
  "url": "https://research.draconitic.com/paper/3rxbs4xqg3fay",
  "venue": "arXiv",
  "versions": []
}