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.

翻译：本文设计了一个由三类代理人组成的微型社会连续重复游戏:个人、保险人和政府。在经济学文献中,我们利用与多武装土匪问题密切相关的加强学习(RL)来了解每花费一美元就拟议的一套政策干预措施对福利的影响。本文认真讨论了拟议干预措施的可取性,在个案基础上对之进行比较,为逻辑政策评估提供了一个框架,使用经过校准的理论模型来协助可行性研究。