用注意力引导策略，让药剂师高效应对药品短缺危机。
Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition
- 通过注意力机制动态划分药品，区分高成本决策与低成本监控
- 模拟显示该策略在长短期场景下均保持稳定决策性能
- 适合研究认知有限下的应急决策或医疗资源分配
医院药剂师在不确定性、时间压力和患者风险下需做出高风险决策以应对药品短缺。访谈发现，药剂师会将注意力集中在少数关键药品上，从而限制认知负担。受此启发，我们提出一种基于有限理性的注意力引导决策框架，动态将药品划分为需高成本推理的子集与互补的低成木监控子集。我们构建了两个智能体：专家智能体采用来自药剂师访谈提取的注意力权重，学习智能体则通过经验自适应调整注意力分配。在涵盖短至长周期的模拟场景中，我们验证了注意力引导规划可在不进行完整状态推理的情况下维持稳定决策。结果表明，核心决策并非选择具体行动，而是如何分配认知资源；注意力引导的满意策略能有效降低问题复杂度，同时保持稳定表现。
原文摘要 · Abstract (English)
Hospital pharmacists make high-stakes decisions to mitigate drug shortages under uncertainty, time pressure, and patient risk. Interviews revealed that pharmacists focus attention on a small subset of drugs, limiting cognitive effort to the most urgent cases. Motivated by these findings, we formalize a bounded-rational, attention-guided decision framework that dynamically decomposes drugs into a subset for high-cost reasoning and a complementary subset for low-cost monitoring. We develop two agents: an Expert Agent that applies attention weights derived from pharmacist interviews, and a Learner Agent that adapts attention allocation over time through experience. Across simulated scenarios spanning short to long horizons, we show that attention-guided planning supports stable decision-making without complete state reasoning. These results suggest that a primary decision is not what action to take, but where to allocate cognitive effort, and that attention-guided, satisficing strategies can reduce problem complexity while maintaining stable performance.
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