Vahana.Jl: A Framework (Not Only) for Large-Scale Agent-Based Models

Agent-based models (ABMs) offer a powerful framework for understanding complex systems. However, their computational demands often become a significant barrier as the number of agents and complexity of the simulation increase. Traditional ABM platforms often struggle to fully exploit modern computing resources, hindering the development of large-scale simulations. This paper presents Vahana.jl, an open-source high-performance computing framework that addresses these limitations through three main contributions: (i) a formalization of the extended graph dynamical system concept as a mathematical foundation for ABMs with dynamic graph structures, (ii) a description of key design decisions in Vahana.jl and their impact on performance and usability, and (iii) an empirical evaluation through two case studies. Building on the formalism of synchronous graph dynamical systems, Vahana.jl is especially well suited for models with a focus on (social) networks. The framework seamlessly supports distribution across multiple compute nodes, enabling simulations that would otherwise be beyond the capabilities of a single machine. Implemented in Julia, Vahana.jl leverages the interactive Read-Eval-Print Loop environment, facilitating rapid model development and experimentation.