aiqbench analysis
This paper proposes a statistical protocol using belief Markov decision processes to optimize sequential randomized controlled trials, aiming to reduce costs in high-stakes domains like drug development while increasing social utility. By modeling the interaction between developers and regulators as a dynamic programming problem, the authors demonstrate that their method can boost social utility by over 35% compared to standard non-sequential protocols. The work sits at the intersection of machine learning methodology and game theory, offering a novel framework for efficient experimentation in regulated industries.
Optimizing Social Utility in Sequential Experiments
arXiv:2605.06520v2 Announce Type: replace-cross 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$$\%$ relative to standard, non-sequential protocols.
Abstract:Regulatory approval of products in high-stakes domains such as drug development requires statistical evidence of safety and efficacy through large-scale randomized controlled trials. However, the high financial cost of these trials may deter developers who lack absolute certainty in their product's efficacy, ultimately stifling the development of `moonshot' products that could offer high social utility. To address this inefficiency, in this paper, we introduce a statistical protocol for experimentation where the product developer (the agent) conducts a randomized controlled trial sequentially and the regulator (the principal) partially subsidizes its cost. By modeling the protocol using a belief Markov decision process, we show that the agent's optimal strategy can be found efficiently using dynamic programming. Further, we show that the social utility is a piecewise linear and convex function over the subsidy level the principal selects, and thus the socially optimal subsidy can also be found efficiently using divide-and-conquer. Simulation experiments using publicly available data on antibiotic development and approval demonstrate that our statistical protocol can be used to increase social utility by more than $35$$\%$ relative to standard, non-sequential protocols.
Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Methodology (stat.ME)
arXiv:2605.06520 [cs.GT]
(or arXiv:2605.06520v2 [cs.GT] for this version)
https://doi.org/10.48550/arXiv.2605.06520 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 283 words
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