abstract: "Public Policies are not intrinsically positive or negative. Rather, policies\
  \ provide varying levels of effects across different recipients. Methodologically,\
  \ computational modeling enables the application of a combination of multiple influences\
  \ on empirical data, thus allowing for heterogeneous response to policies. We use\
  \ a random forest machine learning algorithm to emulate an agent-based model (ABM)\
  \ and evaluate competing policies across 46 Metropolitan Regions (MRs) in Brazil.\
  \ In doing so, we use input parameters and output indicators of 11,076 actual simulation\
  \ runs and one million emulated runs. As a result, we obtain the optimal (and non-optimal)\
  \ performance of each region over the policies. Optimum is defined as a combination\
  \ of GDP production and the Gini coefficient inequality indicator for the full ensemble\
  \ of Metropolitan Regions. Results suggest that MRs already have embedded structures\
  \ that favor optimal or non-optimal results, but they also illustrate which policy\
  \ is more beneficial to each place. In addition to providing MR-specific policies'\
  \ results, the use of machine learning to simulate an ABM reduces the computational\
  \ burden, whereas allowing for a much larger variation among model parameters. The\
  \ coherence of results within the context of larger uncertainty -- vis-\xE0-vis\
  \ those of the original ABM -- suggests an additional test of robustness of the\
  \ model. At the same time the exercise indicates which parameters should policymakers\
  \ intervene, in order to work towards optimum of Metropolitan Regions."
authors:
- affiliation: Institute for Applied Economic Research
  email: bernardo.furtado@ipea.gov.br
  family-names: Furtado
  given-names: Bernardo
  orcid: https://orcid.org/0000-0002-1545-8490
- affiliation: "Hospital de Cl\xEDnicas da Unicamp"
  email: gustavo.93.andreao@gmail.com
  family-names: "Andre\xE3o"
  given-names: Gustavo
cff-version: 1.2.0
identifiers:
- type: url
  value: https://www.comses.net/codebases/bc99c5a5-df36-4c93-aa98-44e226deb387/releases/1.0.0/
keywords:
- Agent-based modeling
- machine learning
- policy
- surrogate
license: CC-BY-4.0
message: If you use this software, please cite it using the metadata from this file.
title: Machine Learning Simulates Agent-based Model Towards Policy
type: software
url: https://www.comses.net/codebases/bc99c5a5-df36-4c93-aa98-44e226deb387/releases/1.0.0/
version: 1.0.0
