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Reinforcement Learning in a New Keynesian Model

Deak, S., Levine, P., Pearlman, J. ORCID: 0000-0001-6301-3966 & Yang, B. (2023). Reinforcement Learning in a New Keynesian Model. Algorithms, 16(6), article number 280. doi: 10.3390/a16060280


We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational (RE) and BR agents. The latter are anticipated utility learners, given their beliefs of aggregate states, and they use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. In the most general form of the model, RE and BR agents learn from their forecasting errors by observing and comparing them with each other, making the composition of the two types endogenous. This reinforcement learning is then at the core of the heterogeneous expectations model and leads to the striking result that increasing the volatility of exogenous shocks, by assisting the learning process, increases the proportion of RE agents and is welfare-increasing.

Publication Type: Article
Additional Information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Publisher Keywords: new Keynesian behavioural model; heterogeneous expectations; bounded rationality; reinforcement learning
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics
Departments: School of Policy & Global Affairs > Economics
SWORD Depositor:
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