Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition
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arXiv:2605.14111v1 Announce Type: new Abstract: 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 su
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Computer Science > Artificial Intelligence
[Submitted on 13 May 2026]
Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition
Yaniv Eliyahu Amiri, Noah Chicoine, Jacqueline Griffin, Stacy Marsella
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.
Comments: Accepted at CogSci 2026. 6 pages plus references, 1 figure, 2 tables
Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2605.14111 [cs.AI]
(or arXiv:2605.14111v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.14111
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Submission history
From: Yaniv Amiri [view email]
[v1] Wed, 13 May 2026 20:53:28 UTC (463 KB)
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