# Surrogate Assisted Methods for the Parameterisation of Agent-Based Models

arXiv: https://arxiv.org/abs/2008.11835 · HTML: https://mlsanity.com/paper/2008.11835/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model)](https://mlsanity.com/paper/2108.08809/) (2021)
- [Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system](https://mlsanity.com/paper/2204.12585/) (2022)
- [A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem](https://mlsanity.com/paper/2304.06675/) (2023)
- [Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem](https://mlsanity.com/paper/2210.17149/) (2022)
- [Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates](https://mlsanity.com/paper/2502.19550/) (2025)
- [Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization](https://mlsanity.com/paper/2609.13247/) (2026)

## Abstract (arXiv)

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 saycurse 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.