Active Inference is an emerging framework providing a quantitative account of behavioral processes in neuroscience and a principled approach to decision-making under uncertainty. Its application to agency problems is natural, offering an autopoietic interpretation of action while addressing classical challenges such as the exploration-exploitation trade-off. Recently, Active Inference has been applied to digital twin scenarios for adaptive and predictive modeling of complex systems. In this work, we extend Active Inference to multi-agent digital twins in which agents interact within a shared environment while maintaining decentralized generative models. Our multi-agent framework features two innovations: (i) contextual inference to improve adaptability in dynamic environments, and (ii) the integration of streaming machine learning within agents' generative structures, enabling tunable goal-oriented behavior while preserving efficiency and scalability. The framework is illustrated through a Cournot competition example, providing a digital twin representation of a socio-economic system and highlighting its potential for coordinated decision-making in multi-agent contexts.

翻译：主动推理是一个新兴框架，它从神经科学角度定量描述了行为过程，并为不确定性下的决策提供了原则性方法。该框架可天然适用于智能体问题，不仅提供了行动的自我生成式解释，还解决了探索-利用权衡等经典挑战。近年来，主动推理已被应用于数字孪生场景，用于复杂系统的自适应与预测性建模。本研究将主动推理扩展至多智能体数字孪生系统——其中各智能体在共享环境中交互，同时保持去中心化的生成模型。我们的多智能体框架包含两项创新：（i）通过情境推理提升动态环境下的适应性；（ii）在智能体生成结构中集成流式机器学习，在保持效率与可扩展性的同时，实现可调的目标导向行为。通过古诺竞争案例演示该框架，为社会经济系统构建了数字孪生表征，并凸显了其在多智能体场景中实现协同决策的潜力。