Optimizing Social Utility in Sequential Experiments
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918488441880576 |
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| author | Velasco, Ander Artola Tsirtsis, Stratis Gomez-Rodriguez, Manuel |
| author_facet | Velasco, Ander Artola Tsirtsis, Stratis Gomez-Rodriguez, Manuel |
| contents | 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$$\%$ relative to standard, non-sequential protocols. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_06520 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimizing Social Utility in Sequential Experiments Velasco, Ander Artola Tsirtsis, Stratis Gomez-Rodriguez, Manuel Computer Science and Game Theory Machine Learning Multiagent Systems Methodology Regulatory approval of products in high-stakes domains such as drug development requires statistical evidence of safety and efficacy through large-scale randomized controlled trials. However, the high financial cost of these trials may deter developers who lack absolute certainty in their product's efficacy, ultimately stifling the development of `moonshot' products that could offer high social utility. To address this inefficiency, in this paper, we introduce a statistical protocol for experimentation where the product developer (the agent) conducts a randomized controlled trial sequentially and the regulator (the principal) partially subsidizes its cost. By modeling the protocol using a belief Markov decision process, we show that the agent's optimal strategy can be found efficiently using dynamic programming. Further, we show that the social utility is a piecewise linear and convex function over the subsidy level the principal selects, and thus the socially optimal subsidy can also be found efficiently using divide-and-conquer. Simulation experiments using publicly available data on antibiotic development and approval demonstrate that our statistical protocol can be used to increase social utility by more than $35$$\%$ relative to standard, non-sequential protocols. |
| title | Optimizing Social Utility in Sequential Experiments |
| topic | Computer Science and Game Theory Machine Learning Multiagent Systems Methodology |
| url | https://arxiv.org/abs/2605.06520 |