Machine Learning simulates Agent-Based Model

Bernardo Alves Furtado
12/12/2017
cs.MA

Official code: BAFurtado/MLsimulatesABM (6 stars, last commit 2020-03-22).

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.

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Code Implementations(1)

BAFurtado/MLsimulatesABMOfficial100%

Tentative mapping from ABM configuration files and ABM outputs with policy goal in mind

63PythonDec 12, 20176 years ago
agent-based-modelingmachine-learningsimulation

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Cite this paper

@article{furtado2017machine,
  title  = {Machine Learning simulates Agent-Based Model},
  author = {Bernardo Alves Furtado},
  year   = {2017},
  eprint = {1712.04429},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1712.04429}
}

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