The SIG on Strongly Empirical Modeling focuses on establishing a deep connection between agent-based models and empirical data. With “empirical data” we mean all types of data which come from the empirical world, such as social media data, survey data, etc. The SIG aims to explore a variety of approaches that might support such an empirical connection including (but not limited to): empirically-based simulation, independent validation checks, pattern-based validation, and cross-validation.

Related to this main research focus, the SIG also aims to:

• Create a network of scholars to shape this research direction
• Connect with external research fields which may share methods and objectives
• Explore and develop standards for the use of empirical data in ABM
• Develop a shared terminology language
• Produce a roadmap, by reviewing existing literature and consulting with the community, highlighting the main challenges and most crucial research objectives
• Organizing events and special issues to increase the impact of this type of research

More information: https://empiricalsig.altervista.org/

Main contact: Dino Carpentras (dino.carpentras[at]gmail.com)