This afternoon, I will give a brief talk on welfare and optimal policies for government intervention, at CCR, in Paris. I have some slides to present. The presentation is based on a papers we wrote a few years ago, Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach
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.
In this paper, we used the concept of Marginal Value of Public Funds, or “MVPF“, introduced in 2020 by Welfare Analysis Meets Causal InferenceA Unified Welfare Analysis of Government Policies“. See some slides online, or the website https://policyinsights.org/.