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似乎作为一个整体移动，展现出从定向运动到旋转再到无序集群的丰富行为谱系。通常，这类宏观模式源于组成成分（例如鱼群中的个体鱼）之间的去中心化局部互动。描述这一过程的著名模型将个体视为自推进粒子，受自生运动和"社会力"（如短程排斥和长程吸引或对齐）的影响。然而，生物体并非粒子；它们是有概率决策能力的智能体。本文提出了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解释为单一目标的后果：最小化惊奇。我们证明，当行为被视作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），许多经验观察到的集体现象（包括凝聚、旋转和定向运动）会自然涌现。此外，我们表明，当智能体试图抑制与其预期相矛盾的预测误差时，主动推理能够恢复并推广社会力的经典概念。通过探索基于信念模型的参数空间，我们揭示了个体信念与群体属性（如极化程度和访问不同集体状态的倾向）之间的非平凡关系。我们还研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，我们展示了智能体如何随时间更新其生成模型，从而使群体对外部波动更敏感，并更稳健地编码信息。