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:
More information: https://empiricalsig.altervista.org/
Main contact: Dino Carpentras (dino.carpentras[at]gmail.com)