Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

Menna Hassan, Nourhan Sakr and Arthur Charpentier

Papers from arXiv.org

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.

Date: 2022-07
New Economics Papers: this item is included in nep-cmp, nep-gth and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.01010

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