ArticleProceedings of the National Academy of Sciences of the United States of America2024
Collective behavior from surprise minimization.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Chaotic Itinerancy in Collective Behaviour Emerging from Active Inference: A Multi-Agent Model of Trust and Empowerment Dynamics in Theatre Workshops.Entropy (Basel, Switzerland) · 2026Article
- Article
- Neuromodulation of swarming behavior inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Noise-induced collective memory in schooling fish.Bioinspiration & biomimetics · 2026Article
- Individual incentives that promote collective intelligence.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Distributionally robust free energy principle for decision-making.Nature communications · 2025Article
- Collective intelligence in animals and robots.Nature communications · 2025Article
- Allocentric flocking.Nature communications · 2025Article
- Neuromodulation of Swarming Behavior inbioRxiv : the preprint server for biology · 2025Article
- Evolution and stability of social learning in animal migration.Movement ecology · 2025Article
- Why collective behaviours self-organize to criticality: a primer on information-theoretic and thermodynamic utility measures.Royal Society open science · 2025Article
- Collective decision making by embodied neural agents.PNAS nexus · 2025Article
- As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference.Entropy (Basel, Switzerland) · 2025Article
- Understanding collective behavior in biological systems through potential field mechanisms.Scientific reports · 2025Article
- Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models.Entropy (Basel, Switzerland) · 2025Article
- Comparing cooperative geometric puzzle solving in ants versus humans.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Formalizing Lacanian psychoanalysis through the free energy principle.Frontiers in psychology · 2025Article
- Body orientation change of neighbors leads to scale-free correlation in collective motion.Nature communications · 2024Article
- Computational joint action: Dynamical models to understand the development of joint coordination.PLoS computational biology · 2024Article
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6 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.