Government Intervention in Catastrophe Insurance Markets

Our paper, Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach, jointly written with Menna Hassan and Nourhan Sakr is now available on ArXiv.

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.


OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (July 5, 2022). Government Intervention in Catastrophe Insurance Markets. Freakonometrics. Retrieved March 23, 2025 from https://doi.org/10.58079/ovjx


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