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 ShortageSim, which 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, \name simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on a self-processed dataset of historical shortage events show that \name 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 \name and a dataset of 2,925 FDA shortage events in https://github.com/Lemutisme/ShortageSim, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.

翻译：药品短缺对全球患者护理和医疗体系构成重大风险，但由于制药供应链中的信息不对称，监管干预措施的有效性仍知之甚少。我们提出ShortageSim，通过提供首个评估信息不对称下监管干预对竞争动态影响的模拟框架，以应对这一挑战。该框架利用基于大语言模型（LLM）的智能体，模拟药品制造商和机构采购方在收到监管机构发布的短缺警报后所采取的战略决策。与假设完全理性和完整信息的传统博弈论模型不同，ShortageSim模拟了对监管公告的异质性解读及其引发的决策。在自行处理的历史短缺事件数据集上的实验表明，ShortageSim将生产中断案例的解决延迟减少了高达84%，相比零样本基线，其模拟结果更贴近现实轨迹。我们的框架证实了监管警报在应对短缺方面的作用，并引入了一种新方法，用于理解不确定性下多阶段环境中的竞争。我们在https://github.com/Lemutisme/ShortageSim开源了ShortageSim框架及包含2,925个FDA短缺事件的数据集，为未来信息不对称下供应链政策设计与测试的研究提供了一个新颖的框架。