Collective behavior pervades biological systems, from flocks of birds to neural assemblies and human societies. Yet, how such collectives acquire functional properties -- such as joint agency or knowledge -- that transcend those of their individual components remains an open question. Here, we combine active inference and information-theoretic analyses to explore how a minimal system of interacting agents can give rise to joint agency and collective knowledge. We model flocking dynamics using multiple active inference agents, each minimizing its own free energy while coupling reciprocally with its neighbors. We show that as agents self-organize, their interactions define higher-order statistical boundaries (Markov blankets) enclosing a ``flock'' that can be treated as an emergent agent with its own sensory, active, and internal states. When exposed to external perturbations (a ``predator''), the flock exhibits faster, coordinated responses than individual agents, reflecting collective sensitivity to environmental change. Crucially, analyses of synergistic information reveal that the flock encodes information about the predator's location that is not accessible to every individual bird, demonstrating implicit collective knowledge. Together, these results show how informational coupling among active inference agents can generate new levels of autonomy and inference, providing a framework for understanding the emergence of (implicit) collective knowledge and joint agency.

翻译：集体行为普遍存在于生物系统中，从鸟群到神经集群再到人类社会。然而，这样的集体如何获得超越其个体组成部分的功能性属性——例如联合能动性或知识——仍然是一个悬而未决的问题。在此，我们结合主动推理与信息论分析，探索一个由相互作用的智能体构成的最小系统如何能够产生联合能动性与集体知识。我们使用多个主动推理智能体对集群动态进行建模，每个智能体在最小化其自身自由能的同时，与其邻居进行互惠耦合。我们证明，随着智能体的自组织，它们的相互作用定义了更高阶的统计边界（马尔可夫毯），这个边界围成了一个可被视为一个涌现智能体的“群体”，该智能体拥有其自身的感知状态、活动状态和内部状态。当受到外部扰动（一个“捕食者”）时，群体表现出比个体智能体更快、更协调的响应，这反映了集体对环境变化的敏感性。至关重要的是，对协同信息的分析表明，群体编码了关于捕食者位置的信息，而这些信息并非每只鸟都能获取，这证明了隐性的集体知识。总之，这些结果表明了主动推理智能体之间的信息耦合如何能够产生新的自主性与推理层级，为理解（隐性）集体知识与联合能动性的涌现提供了一个框架。