ShortageSim: 在信息不对称下模拟药品短缺
ShortageSim: Simulating Drug Shortages under Information Asymmetry
AI总结：
ShortageSim通过模拟信息不对称下的药品短缺情况，提供了一个评估监管干预效果的框架，减少了生产中断的解决延迟，推动了供应链政策研究的发展。
摘要：
药品短缺对全球患者护理和医疗系统构成了重大风险，然而由于制药供应链中的信息不对称，监管干预的有效性仍不明确。我们提出了ShortageSim，这是首个评估监管干预对信息不对称下竞争动态影响的模拟框架。利用基于大语言模型（LLM）的智能体，该框架模拟了药品制造商和机构买家对监管机构发布的短缺警报的战略决策。不同于传统博弈论模型假设完全理性与完整信息，ShortageSim模拟了对监管公告的异质解读及其决策结果。在自处理的历史短缺事件数据集上进行的实验表明，ShortageSim将生产中断案例的解决延迟时间减少了高达84%，比零样本基线更接近现实世界轨迹。我们的框架证实了监管警报在缓解短缺中的作用，并引入了一种在不确定环境下多阶段环境中理解竞争的新方法。我们开源了ShortageSim和包含2925个FDA短缺事件的数据集，为未来在信息不对称下供应链中的政策设计和测试提供了新的研究框架。
英文摘要：
Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to information asymmetries in pharmaceutical supply chains. We propose \textbf{ShortageSim}, addresses this challenge by providing the first simulation framework that evaluates the impact of regulatory interventions on competition dynamics under information asymmetry. Using Large Language Model (LLM)-based agents, the framework models the strategic decisions of drug manufacturers and institutional buyers, in response to shortage alerts given by the regulatory agency. Unlike traditional game theory models that assume perfect rationality and complete information, ShortageSim simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on self-processed dataset of historical shortage events show that ShortageSim reduces the resolution lag for production disruption cases by up to 84\%, achieving closer alignment to real-world trajectories than the zero-shot baseline. Our framework confirms the effect of regulatory alert in addressing shortages and introduces a new method for understanding competition in multi-stage environments under uncertainty. We open-source ShortageSim and a dataset of 2,925 FDA shortage events, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.
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