Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition

TL;DR

This paper introduces an attention-guided decision framework for pharmacists managing drug shortages, emphasizing cognitive effort allocation to improve decision stability under uncertainty.

Contribution

It formalizes a bounded-rational, attention-guided model with adaptive and fixed attention agents, demonstrating effective decision-making with reduced complexity.

Findings

Attention-guided planning supports stable decisions without full state reasoning.

Attention allocation can be learned and adapted over time.

Strategies focusing on where to allocate effort are effective in complex decisions.

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 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.