论文标题
基于代理模型的参数化的替代辅助方法
Surrogate Assisted Methods for the Parameterisation of Agent-Based Models
论文作者
论文摘要
参数校准是基于代理的建模和仿真（ABM）的主要挑战。随着基于代理模型（ABM）的复杂性的增加，需要校准的参数数量就会增长。这导致了\ say {dimensionalityality}的abms等效。我们提出了一个ABMS框架，该框架促进了不同采样方法和替代模型（SMS）的有效整合，以评估这些策略如何影响参数校准和探索。我们表明，替代辅助方法的性能优于标准采样方法。此外，我们表明XGBoost和决策树SMS在我们的分析方面最佳。
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.
