Deep Reinforcement Learning in a Search-Matching Model of Labor Market Fluctuations

Ruxin Chen ()
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Ruxin Chen: Department of Economics, Nagoya University, Aichi 464-8601, Japan

Economies, 2025, vol. 13, issue 10, 1-16

Abstract: Shimer documents that the search-and-matching model driven by productivity shocks explains only a small share of the observed volatility of unemployment and vacancies, which is known as the Shimer puzzle. We revisit this evidence by replacing the representative firm’s optimization with a deep reinforcement learning (DRL) agent that learns its vacancy-posting policy through interaction in a Diamond–Mortensen–Pissarides (DMP) model. Comparing the learning economy with a conventional log-linearized DSGE solution under the same parameters, we find that while both frameworks preserve a downward-sloping Beveridge curve, learning-based economy produces much higher volatility in key labor market variables and returns to a steady state more slowly after shocks. These results point to bounded rationality and endogenous learning as mechanisms for labor market fluctuations and suggest that reinforcement learning can serve as a useful complement to standard macroeconomic analysis.

Keywords: search-and-matching model; labor market simulation; macroeconomic modeling; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2025
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