Agent-based model inverse problem

The Agent-based model inverse problem refers to the process of inferring the parameters of an agent-based model that can accurately simulate observed system behavior data. The goal is to uncover the internal interaction mechanisms and individual behavioral rules of complex systems, thereby enhancing the predictive power and interpretability of the model. This problem has significant application value in fields such as socio-economic systems, ecological modeling, and traffic flow analysis, contributing to a deeper understanding of system dynamics and the optimization of management strategies.