Abstract
Agent-based models (ABM) are valuable for modelling complex systems, however, they are often manually specified and lack behavioral and/or environmental adaptation. In this work, we develop a generic two-layer framework for ADaptive AGEnt based modelling (ADAGE) for addressing this. ADAGE formalises the bi-level problem of agent and environment adaptation as a Stackelberg game, providing a consolidated framework for adaptive ABM. We demonstrate how ADAGE encapsulates several modelling tasks, such as policy design, calibration, scenario generation, and robust behavioural learning under one unified framework. We provide example simulations on various environments, showing the flexibility of ADAGE while addressing long-standing critiques of ABMs.