Action subsampling supports policy compression in large action spaces

Real-world decision-making often involves navigating large action spaces with state-dependent action values, taxing the limited cognitive resources at our disposal. While previous studies have explored cognitive constraints on generating action consideration sets or refining state-action mappings (policy complexity), their interplay remains underexplored. In this work, we present a resource-rational framework for policy compression that integrates both constraints, offering a unified perspective on decision-making under cognitive limitations. Through simulations, we characterize the suboptimality arising from reduced action consideration sets and reveal the complex interaction between policy complexity and action consideration set size in mitigating this suboptimality. We then use such normative insight to explain empirically observed phenomena in option generation, including the preferential sampling of generally valuable options and increased correlation in responses across contexts under cognitive load. We further validate the framework's predictions through a contextual multi-armed bandit experiment, showing how humans flexibly adapt their action consideration sets and policy complexity to maintain near-optimality in a task-dependent manner. Our study demonstrates the importance of accounting for fine-grained resource constraints in understanding human cognition, and highlights the presence of adaptive metacognitive strategies even in simple tasks.