Novel Exploration Techniques (NETs) for Malaria Policy Interventions
  The task of decision-making under uncertainty is daunting, especially for
problems which have significant complexity. Healthcare policy makers across the
globe are facing problems under challenging constraints, with limited tools to
help them make data driven decisions. In this work we frame the process of
finding an optimal malaria policy as a stochastic multi-armed bandit problem,
and implement three agent based strategies to explore the policy space. We
apply a Gaussian Process regression to the findings of each agent, both for
comparison and to account for stochastic results from simulating the spread of
malaria in a fixed population. The generated policy spaces are compared with
published results to give a direct reference with human expert decisions for
the same simulated population. Our novel approach provides a powerful resource
for policy makers, and a platform which can be readily extended to capture
future more nuanced policy spaces.
