Heins, C., Millidge, B., Da Costa, L. et al. (3 more authors) (2024) Collective behavior from surprise minimization. Proceedings of the National Academy of Sciences of USA, 121 (17). e2320239121. ISSN 0027-8424
Abstract
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and “social forces” such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference—without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). |
Keywords: | Collective motion; Active inference; Agent-based models; Bayesian inference; Animal behavior |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Funding Information: | Funder Grant number MRC (Medical Research Council) MR/S032525/1 Templeton World Charity Foundation UR FAO GR533391 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Mar 2024 10:24 |
Last Modified: | 18 Apr 2024 14:25 |
Status: | Published |
Publisher: | National Academy of Sciences |
Identification Number: | 10.1073/pnas.2320239121 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210480 |
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