Multi-Agent Digital Twins for Strategic Decision-Making using Active Inference

2026-04-14

AI Summary

This work addresses the challenges of coordinated decision-making, exploration–exploitation trade-offs, and real-time adaptation faced by multi-agent systems in dynamic, complex environments. The authors propose a multi-agent digital twin framework grounded in active inference, leveraging decentralized generative models to mediate agent interactions. By integrating contextual reasoning with streaming machine learning, the framework enables efficient, scalable, and goal-adaptive behavioral policies. This approach substantially enhances the system’s capacity to adapt to environmental changes. The framework’s efficacy and practical potential are demonstrated through a Cournot competition case study, where a digital twin of a socioeconomic system is successfully constructed, validating its capability to support effective multi-agent collaborative decision-making.

Technology Category

• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multi-Robot Systems

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Multi-Agent Digital Twins
• Strategic Decision-Making
• Active Inference
• Decentralized Generative Models
• Dynamic Environments

Innovation

• Active Inference
• Multi-Agent Digital Twins
• Contextual Inference
• Streaming Machine Learning
• Decentralized Generative Models