Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A vast space of compact strategies for highly efficient decisions

View ORCID ProfileTzuhsuan Ma, View ORCID ProfileAnn M Hermundstad
doi: https://doi.org/10.1101/2022.08.10.503471
Tzuhsuan Ma
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tzuhsuan Ma
  • For correspondence: mat@janelia.hhmi.org hermundstada@janelia.hhmi.org
Ann M Hermundstad
1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ann M Hermundstad
  • For correspondence: mat@janelia.hhmi.org hermundstada@janelia.hhmi.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

ABSTRACT

When foraging in dynamic and uncertain environments, animals can benefit from basing their decisions on smart inferences about hidden properties of the world. Typical theoretical approaches to understand the strategies that animals use in such settings combine Bayesian inference and value iteration to derive optimal behavioral policies that maximize total reward given changing beliefs about the environment. However, specifying these beliefs requires infinite numerical precision; with limited resources, this problem can no longer be separated into optimizing inference and optimizing action selections. To understand the space of behavioral policies in this constrained setting, we enumerate and evaluate all possible behavioral programs that can be constructed from just a handful of states. We show that only a small fraction of the top-performing programs can be constructed by approximating Bayesian inference; the remaining programs are structurally or even functionally distinct from Bayesian. To assess structural and functional relationships among all programs, we developed novel tree embedding algorithms; these embeddings, which are capable of extracting different relational structures within the program space, reveal that nearly all good programs are closely connected through single algorithmic “mutations”. We demonstrate how one can use such relational structures to efficiently search for good solutions via an evolutionary algorithm. Moreover, these embeddings reveal that the diversity of non-Bayesian behaviors originates from a handful of key mutations that broaden the functional repertoire within the space of good programs. The fact that this diversity of behaviors does not significantly compromise performance suggests a novel approach for studying how these strategies generalize across tasks.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/HermundstadLab/ProgEnum

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted August 13, 2022.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A vast space of compact strategies for highly efficient decisions
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A vast space of compact strategies for highly efficient decisions
Tzuhsuan Ma, Ann M Hermundstad
bioRxiv 2022.08.10.503471; doi: https://doi.org/10.1101/2022.08.10.503471
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A vast space of compact strategies for highly efficient decisions
Tzuhsuan Ma, Ann M Hermundstad
bioRxiv 2022.08.10.503471; doi: https://doi.org/10.1101/2022.08.10.503471

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Animal Behavior and Cognition
Subject Areas
All Articles
  • Animal Behavior and Cognition (4095)
  • Biochemistry (8793)
  • Bioengineering (6495)
  • Bioinformatics (23407)
  • Biophysics (11769)
  • Cancer Biology (9173)
  • Cell Biology (13304)
  • Clinical Trials (138)
  • Developmental Biology (7426)
  • Ecology (11392)
  • Epidemiology (2066)
  • Evolutionary Biology (15128)
  • Genetics (10419)
  • Genomics (14030)
  • Immunology (9154)
  • Microbiology (22133)
  • Molecular Biology (8797)
  • Neuroscience (47470)
  • Paleontology (350)
  • Pathology (1423)
  • Pharmacology and Toxicology (2486)
  • Physiology (3712)
  • Plant Biology (8073)
  • Scientific Communication and Education (1434)
  • Synthetic Biology (2217)
  • Systems Biology (6023)
  • Zoology (1251)