Finding Regularized Competitive Equilibria of Heterogeneous Agent
Macroeconomic Models with Reinforcement Learning
- URL: http://arxiv.org/abs/2303.04833v1
- Date: Fri, 24 Feb 2023 17:16:27 GMT
- Title: Finding Regularized Competitive Equilibria of Heterogeneous Agent
Macroeconomic Models with Reinforcement Learning
- Authors: Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan,
Zhuoran Yang
- Abstract summary: We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market.
We propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model.
- Score: 151.03738099494765
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study a heterogeneous agent macroeconomic model with an infinite number of
households and firms competing in a labor market. Each household earns income
and engages in consumption at each time step while aiming to maximize a concave
utility subject to the underlying market conditions. The households aim to find
the optimal saving strategy that maximizes their discounted cumulative utility
given the market condition, while the firms determine the market conditions
through maximizing corporate profit based on the household population behavior.
The model captures a wide range of applications in macroeconomic studies, and
we propose a data-driven reinforcement learning framework that finds the
regularized competitive equilibrium of the model. The proposed algorithm enjoys
theoretical guarantees in converging to the equilibrium of the market at a
sub-linear rate.
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