A Comprehensive Framework for Data-Driven Agent-Based Modeling

Integrating data-driven methodologies with agent-based simulation presents an opportunity to automate modeling and enable Digital Twins for complex systems. This integration allows for utilization of real-world data to extract models that update with changes in the corresponding real systems and enhance our abilities to make informed decisions. We were unable to identify a systematic approach for developing data-driven agent-based models beyond isolated attempts focused on specific aspects. In response, we reviewed existing literature to develop a framework that systematically approaches data-driven agent-based modeling. We believe that our framework can assist in systematically evaluating which parts of agent-based models' development processes can be data-driven. Furthermore, we provide a comprehensive exploration of data-driven methods that can be applied to each stage of the model development process. Finally, we utilize our prior works in this area to demonstrate the application of data-driven methodologies in capturing patterns and insights for model development.