World models of agent societies

Core objective

A central long-term goal of imec.AI-labs is to develop the ability to model complex societies of interacting agents that communicate in natural language. The aim is to predict how these agent societies will evolve over time by capturing how distributed groups of agents exchange information, update beliefs, react to external conditions, and collectively shape future outcomes.

Broad applications

By successfully learning “World Models” of agent societies, researchers aim to unlock capabilities across several domains:

• Forecasting dynamics: predicting large-scale economic and social trajectories.
• System design: designing multi-agent AI systems capable of coordinating, negotiating, and making collective decisions.
• Simulation and counterfactual reasoning: creating robust simulation environments to study policy interventions, governance, and technological shifts. This includes exploring how alternative communication patterns, incentive structures, or environmental pressures might alter a society’s long-term path.

Ultimately, this research aims to bridge the gap between micro-level agent behaviors and macro-level emergent phenomena, providing a principled framework to understand and steer complex sociotechnical systems.