Analyzing Decision-Making in Cognitive Agent Simulations Using Generalized Linear Mixed-Effects Models

Enhancing model interpretability remains an ongoing challenge in predictive modelling, especially when applied to simulation data from complex systems. Investigating the influence and effects of design factors within computer simulations of complex systems requires assessing variable importance through statistical models. These models are crucial for capturing the relationships between factors and response variables. This study focuses on understanding functional patterns and their magnitudes of influence regarding designed factors affecting cognitive agent decision-making in a cellular automaton-based highway crossing simulation. We aim to identify the most influential design factors in the complex system simulation model to better understand the relationship between the decision outcomes and the designed factors. We apply Generalized Linear Mixed-Effects Models to explain the significant functional connections between designed factors and response variables, specifically quantifying variable importance. Our analysis demonstrates the practicality and effectiveness of the proposed models and methodologies for analyzing data from complex systems. The findings offer a deeper understanding of the connections between design factors and their resulting responses, facilitating a greater understanding of the underlying dynamics and contributing to the fields of applied mathematics, simulation modelling, and computation.