Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2207.01010v1
• Date: Sun, 3 Jul 2022 11:06:44 GMT
• Title: Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
• Authors: Menna Hassan, Nourhan Sakr and Arthur Charpentier
• Abstract summary: 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.
• Score: 0.04297070083645048
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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