# Agent-Based Model Calibration using Machine Learning Surrogates

## TL;DR

This paper introduces a machine learning surrogate approach for efficient calibration and exploration of agent-based models, significantly reducing computational costs and enhancing understanding of complex model behaviors.

## Contribution

It presents a novel method combining supervised machine learning and intelligent sampling to create accurate surrogates for ABMs, enabling large-scale parameter exploration and calibration.

## Key findings

- Surrogate models significantly reduce computation time.
- Method accurately captures complex ABM behaviors.
- Effective in calibrating models like Brock-Hommes and endogenous growth.

## Abstract

Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate meta-model. The proposed approach provides a fast and accurate approximation of model behaviour, dramatically reducing computation time. In that, our machine-learning surrogate facilitates large scale explorations of the parameter-space, while providing a powerful filter to gain insights into the complex functioning of agent-based models. The algorithm introduced in this paper merges model simulation and output analysis into a surrogate meta-model, which substantially ease ABM calibration. We successfully apply our approach to the Brock and Hommes (1998) asset pricing model and to the "Island" endogenous growth model (Fagiolo and Dosi, 2003). Performance is evaluated against a relatively large out-of-sample set of parameter combinations, while employing different user-defined statistical tests for output analysis. The results demonstrate the capacity of machine learning surrogates to facilitate fast and precise exploration of agent-based models' behaviour over their often rugged parameter spaces.