Surrogate Assisted Methods for the Parameterisation of Agent-Based Models
This work addresses the curse of dimensionality in agent-based modeling and simulation, offering improved calibration methods for researchers in computational social sciences or similar domains, though it appears incremental as it builds on existing surrogate model techniques.
The paper tackles the challenge of parameter calibration in agent-based models (ABMs) by proposing a framework that integrates sampling methods and surrogate models, showing that surrogate assisted methods outperform standard sampling methods and identifying XGBoost and Decision Tree as the most optimal surrogate models.
Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to be calibrated grows. This leads to the ABMS equivalent of the \say{curse of dimensionality}. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.