  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 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-\`a-vis those of the original ABM--reinforces robustness of
the model. At the same time the exercise indicates which parameters should
policymakers intervene on, in order to work towards precise policy optimal
instruments.
