PDF(Pubmed)
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.
摘要：
集体运动在自然界中无处不在；一群动物，比如鱼,鸟,有蹄类动物似乎作为一个整体移动，表现出丰富的行为方式，范围从定向运动到铣削到无序的蜂群。通常,这种宏观模式源于分散，组成成分之间的局部相互作用(例如，学校里的个体鱼)。这个过程的杰出模型将个体描述为自我推进的粒子，受到自我产生的运动和“社会力量”的影响，例如短程排斥和远程吸引或对准。然而,有机体不是粒子；它们是概率决策者。这里,我们介绍了一种基于主动推理的集体行为建模方法。这种认知框架将行为投射为单个命令的结果：最小化惊喜。我们证明了许多经验观察到的集体现象，包括凝聚力，铣削,和定向运动，当考虑由主动贝叶斯推理驱动的行为时，自然会出现-没有明确地将行为规则或目标构建到个体代理中。此外，我们证明，当代理人试图抑制与他们的期望相冲突的预测错误时，主动推理可以恢复和推广社会力量的经典概念。通过探索基于信念模型的参数空间，我们揭示了个人信念和群体属性之间的非平凡关系，例如极化和访问不同集体状态的趋势。我们还探讨了个人对不确定性的信念如何决定集体决策的准确性。最后,我们展示了代理人如何随着时间的推移更新他们的生成模型，导致群体共同对外部波动更敏感，信息编码更稳健。
