Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
Multiagent Systems
2022-07-05 v1 Machine Learning
General Economics
Economics
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.
Keywords
Cite
@article{arxiv.2207.01010,
title = {Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach},
author = {Menna Hassan and Nourhan Sakr and Arthur Charpentier},
journal= {arXiv preprint arXiv:2207.01010},
year = {2022}
}