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 modelling 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 non-trivial 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.

翻译：集体运动在自然界中普遍存在；鱼类、鸟类和有蹄类等动物群体看似作为一个整体移动，展现出从定向运动到环形运动再到无序集群的丰富行为谱系。通常，这类宏观模式源于个体组成单元（例如鱼群中的单条鱼）之间的去中心化局部交互。描述该过程的经典模型将个体视为自驱动粒子，受自发运动及"社会力"（如短程排斥与长程吸引或对齐）的支配。然而，生物体并非粒子，而是概率决策者。本文提出一种基于主动推理的集体行为建模方法。这一认知框架将行为诠释为单一驱动原则——最小化惊奇——的结果。我们证明，当将行为视为由主动贝叶斯推理驱动时（无需在个体智能体中显式构建行为规则或目标），许多实验观测到的集体现象（包括凝聚、环形运动和定向运动）会自然涌现。此外，我们揭示主动推理能够恢复并泛化社会力的经典概念——个体通过抑制与预期冲突的预测误差来维持群体协调。通过探索基于信念模型的参数空间，我们揭示了群体极化、不同集体状态访问倾向等群体属性与个体信念之间的非平凡关系。我们还探究了关于不确定性的个体信念如何决定集体决策的准确性。最后，我们展示个体如何随时间更新其生成模型，从而使群体对外部波动具有更高的集体敏感性，并能更稳健地编码信息。