Data-driven Agent-based Modeling: Experimenting with the Schelling's Model

Agent-based modeling and simulation is a practical computational technique for studying complex systems of autonomous agents in various disciplines. Agent-based models facilitate the study of emergent phenomena by simulating heterogeneous agents and their flexible behaviors and interactions. However, developing an agent-based model of a complex system is often time-consuming and vulnerable to the modeler's biases. Addressing this challenge requires a paradigm shift from knowledge-driven modeling to data-driven modeling. In this research, we initiate and experiment with automating the process of composing agent-based models by developing data-driven model extraction. To achieve this objective, we conduct experiments employing different variations of Schelling's segregation model, a well-known agent-based model, each featuring different parameter sets and complexity levels.