Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

Menna Hassan; Nourhan Sakr; Arthur Charpentier

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arXiv · 2207.01010

Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

Menna Hassan·Nourhan Sakr·Arthur Charpentier

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Abstract

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.

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Menna Hassan, Nourhan Sakr, Arthur Charpentier. 2022-07-03. Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach. https://arxiv.org/abs/2207.01010

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