Exploring Data Requirements for Data-Driven Agent-Based Modeling

Extracting Agent-Based Models (ABMs) from data, also known as Data-Driven Agent-Based Modeling (DDABM), requires a clear understanding of data requirements and their mappings to the corresponding ABM components. DDABM is a relatively new and emerging topic, and as such, there are only highly customized and problem-specific solutions and approaches. In our previous work, we presented a framework for DDABM, identifying the different components of ABMs that can be extracted from data. Building on this, the present study provides a comprehensive analysis of existing DDABM approaches, examining prevailing trends and methodologies, focusing on the mappings between data and ABM components. By synthesizing and comparing different DDABM approaches, we establish explicit mappings that clarify data requirements and their role in enabling DDABM. Our findings enhance the understanding of DDABM and highlight the role of data in automating model extraction, highlighting its potential for advancing data-driven agent-based simulations.