A vast space of compact strategies for effective decisions

Abstract

Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral “program” that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous—comprising a quarter million programs with up to five internal states—but can nevertheless be understood in terms of algorithmic “mutations” that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.