Machine Learning simulates Agent-Based Model

TL;DR

This paper demonstrates that machine learning can effectively predict optimal configurations of agent-based models, aiding in parameter calibration and providing insights into socioeconomic factors affecting quality of life.

Contribution

It introduces a method to use machine learning for predicting optimal ABM configurations and analyzing parameter influence, enhancing understanding and efficiency.

Findings

Machine learning accurately predicts optimal ABM parameter sets.

Parameter analysis reveals key factors influencing model outcomes.

Method helps identify regions with higher quality of life.

Abstract

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.