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Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

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  • Menna Hassan
  • Nourhan Sakr
  • Arthur Charpentier

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.

Suggested Citation

  • Menna Hassan & Nourhan Sakr & Arthur Charpentier, 2022. "Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach," Papers 2207.01010, arXiv.org.
    • Handle: RePEc:arx:papers:2207.01010
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    References listed on IDEAS

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