Running agent-based models (ABMs) is a burdensome computational task, specially so when considering the flexibility ABMs intrinsically provide. This paper uses a bundle of model configuration parameters along with obtained results from a validated ABM to train some Machine Learning methods for socioeconomic optimal cases. A larger space of possible parameters and combinations of parameters are then used as input to predict optimal cases and confirm parameters calibration. Analysis of the parameters of the optimal cases are then compared to the baseline model. This exploratory initial exercise confirms the adequacy of most of the parameters and rules and suggests changing of directions to two parameters. Additionally, it helps highlight metropolitan regions of higher quality of life. Better understanding of ABM mechanisms and parameters' influence may nudge policy-making slightly closer to optimal level.

中文翻译：

---

机器学习模拟基于代理的模型

运行基于代理的模型 (ABM) 是一项繁重的计算任务，尤其是在考虑 ABM 本质上提供的灵活性时。本文使用一组模型配置参数以及从经过验证的 ABM 获得的结果来训练一些用于社会经济最优案例的机器学习方法。然后使用更大空间的可能参数和参数组合作为输入来预测最佳情况并确认参数校准。然后将最佳情况的参数分析与基线模型进行比较。这个探索性的初始练习证实了大多数参数和规则的充分性，并建议将方向更改为两个参数。此外，它还有助于突出生活质量更高的大都市地区。更好地了解 ABM 机制和参数