Collective behavior from surprise minimization.

Autor:	Heins C; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.; Department of Biology, University of Konstanz, Konstanz D-78457, Germany.; VERSES Research Lab, Los Angeles, CA 90016., Millidge B; Medical Research Council Brain Networks Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom., Da Costa L; VERSES Research Lab, Los Angeles, CA 90016.; Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom.; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom., Mann RP; Department of Statistics, School of Mathematics, University of Leeds, Leeds LS2 9JT, United Kingdom., Friston KJ; VERSES Research Lab, Los Angeles, CA 90016.; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom., Couzin ID; Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany.; Department of Biology, University of Konstanz, Konstanz D-78457, Germany.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Apr 23; Vol. 121 (17), pp. e2320239121. Date of Electronic Publication: 2024 Apr 17.
DOI:	10.1073/pnas.2320239121
Abstrakt:	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. Competing Interests: Competing interests statement:The authors declare no competing interest.
Databáze:	MEDLINE
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