论文标题
政府干预灾难保险市场：一种加强学习方法
Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
论文作者
论文摘要
本文设计了一个由三种类型的代理人：个人，保险公司和政府的依次重复游戏。对经济学文献的新生，我们使用加强学习（RL），与多军匪徒问题密切相关，以学习每花费1美元的拟议政策干预措施的福利影响。本文严格讨论了提议的干预措施的可取性，通过将它们逐案相互比较。本文为校准的理论模型提供了算法政策评估的框架，该模型可以帮助可行性研究。
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.
