A Novel Multi-Scale Agent-Based Modeling Framework for Simulating Complex Adaptive Systems in Urban Environments

Urban environments are increasingly recognized as complex adaptive systems, where dynamic interactions between heterogeneous agents—such as individuals, organizations, infrastructure, and environmental components—give rise to emergent behaviors that are difficult to predict using conventional modeling techniques. This paper introduces a novel multi-scale agent-based modeling (MS-ABM) framework designed to capture and simulate these interactions across multiple spatial and temporal resolutions. The proposed framework integrates micro-level behavioral rules with macro-level system constraints, enabling the simultaneous analysis of individual agent decisions and large-scale urban phenomena such as traffic flow, land-use evolution, and resource distribution. A hierarchical communication mechanism is developed to enable bidirectional information exchange between scales, improving model fidelity and responsiveness. The framework is validated using a case study of urban mobility in a rapidly growing metropolitan region, demonstrating its ability to reproduce real-world patterns, adapt to dynamic policy interventions, and support scenario-based decision making. The results highlight the potential of MS-ABM as a robust tool for urban planners, policy makers, and researchers to explore the interplay of local behaviors and global outcomes in complex urban systems.