# Machine Learning simulates Agent-Based Model

arXiv: https://arxiv.org/abs/1712.04429 · HTML: https://mlsanity.com/paper/1712.04429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 1 of 1)

- [Machine Learning Simulates Agent-Based Model Towards Policy](https://mlsanity.com/paper/2203.02576/) (2022)

## Abstract (arXiv)

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.