Resource-rational Task Decomposition to Minimize Planning Costs
This work addresses a fundamental question in cognitive science about human planning efficiency, offering a framework for studying reasoning and action, but it is incremental as it builds on prior work on task decomposition.
The paper tackles the problem of why people decompose tasks hierarchically by formalizing it as a resource-rational representation problem, proposing that decomposition optimizes cognitive resource use given environmental structure and planning algorithms, and replicates existing findings to provide a normative explanation.
People often plan hierarchically. That is, rather than planning over a monolithic representation of a task, they decompose the task into simpler subtasks and then plan to accomplish those. Although much work explores how people decompose tasks, there is less analysis of why people decompose tasks in the way they do. Here, we address this question by formalizing task decomposition as a resource-rational representation problem. Specifically, we propose that people decompose tasks in a manner that facilitates efficient use of limited cognitive resources given the structure of the environment and their own planning algorithms. Using this model, we replicate several existing findings. Our account provides a normative explanation for how people identify subtasks as well as a framework for studying how people reason, plan, and act using resource-rational representations.