Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

2022-07-03 · Menna Hassan, Nourhan Sakr, Arthur Charpentier

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.

📄 PDF Abstract BibTeX arXiv:2207.01010

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning

2025-06-16 · Stella C. Dong, James R. Finlay

This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Atari Games reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

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