A vast space of compact strategies for highly efficient decisions

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Sci. Adv.. 2024 Jun 21;10(25). doi: 10.1101/2022.08.10.503471

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A vast space of compact strategies for highly efficient decisions Hermundstad Lab

Ma Tzuhsuan, Hermundstad Ann

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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.

PMID: 38905348 [PubMed - indexed for MEDLINE]

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bioRxiv PrePrint https://doi.org/10.1101/2022.08.10.503471

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A vast space of compact strategies for highly efficient decisions

Abstract

PMID: 38905348 [PubMed - indexed for MEDLINE]

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