Policy4OOD：一种知识引导的世界模型，用于针对阿片类药物过量危机的政策干预模拟
Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
AI总结：
提出Policy4OOD，一种知识引导的时空世界模型，通过联合编码政策知识图谱、空间依赖和时间序列，实现政策干预的预测、反事实推理和优化，并在2019-2024年美国州级数据上验证了其有效性。
摘要：
阿片类药物流行仍然是美国最严重的公共卫生危机之一，然而在实施前评估政策干预措施是困难的：多种政策在一个动态系统中相互作用，针对一个风险途径可能会无意中放大另一个。我们认为，有效的阿片类药物政策评估需要三种能力——在当前政策下预测未来结果、关于过去替代决策的反事实推理以及候选干预措施的优化——并提议通过世界建模将它们统一起来。我们引入了Policy4OOD，一种知识引导的时空世界模型，它解决了三个核心挑战：政策规定了什么、效果在哪里显现以及效果何时展开。Policy4OOD将政策知识图谱、州级空间依赖和社会经济时间序列联合编码到一个政策条件Transformer中，用于预测未来的阿片类药物结果。训练完成后，世界模型作为一个模拟器：预测只需一次前向传递，反事实分析在历史序列中替换替代政策编码，而政策优化则使用蒙特卡洛树搜索在学习的模拟器上进行。为了支持这一框架，我们构建了一个整合了阿片类药物死亡率、社会经济指标和结构化政策编码的州级月度数据集（2019-2024）。实验表明，空间依赖和结构化政策知识显著提高了预测准确性，验证了每个架构组件以及世界建模在数据驱动的公共卫生决策支持中的潜力。我们的代码和数据已发布在https://this URL。
英文摘要：
The opioid epidemic remains one of the most severe public health crises in the United States, yet evaluating policy interventions before implementation is difficult: multiple policies interact within a dynamic system where targeting one risk pathway may inadvertently amplify another. We argue that effective opioid policy evaluation requires three capabilities -- forecasting future outcomes under current policies, counterfactual reasoning about alternative past decisions, and optimization over candidate interventions -- and propose to unify them through world modeling. We introduce Policy4OOD, a knowledge-guided spatio-temporal world model that addresses three core challenges: what policies prescribe, where effects manifest, and when effects unfold.Policy4OOD jointly encodes policy knowledge graphs, state-level spatial dependencies, and socioeconomic time series into a policy-conditioned Transformer that forecasts future opioid outcomes.Once trained, the world model serves as a simulator: forecasting requires only a forward pass, counterfactual analysis substitutes alternative policy encodings in the historical sequence, and policy optimization employs Monte Carlo Tree Search over the learned simulator. To support this framework, we construct a state-level monthly dataset (2019--2024) integrating opioid mortality, socioeconomic indicators, and structured policy encodings. Experiments demonstrate that spatial dependencies and structured policy knowledge significantly improve forecasting accuracy, validating each architectural component and the potential of world modeling for data-driven public health decision support. Our code and data have been released in https://github.com/antman9914/Policy4OOD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
