  This paper designs a sequential repeated game of a micro-founded society with
three types of agents: individuals, insurers, and a government. Nascent to
economics literature, we use Reinforcement Learning (RL), closely related to
multi-armed bandit problems, to learn the welfare impact of a set of proposed
policy interventions per $1 spent on them. The paper rigorously discusses the
desirability of the proposed interventions by comparing them against each other
on a case-by-case basis. The paper provides a framework for algorithmic policy
evaluation using calibrated theoretical models which can assist in feasibility
studies.
