Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach

 0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Arthur Charpentier, Menna Hassan, Nourhan Sakr

Ngôn ngữ: eng

Ký hiệu phân loại: 351.754 Public administration

Thông tin xuất bản: 2022

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 195548

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.
Tạo bộ sưu tập với mã QR

THƯ VIỆN - TRƯỜNG ĐẠI HỌC CÔNG NGHỆ TP.HCM

ĐT: (028) 36225755 | Email: tt.thuvien@hutech.edu.vn

Copyright @2024 THƯ VIỆN HUTECH