Labor Market Dynamics and Unemployment Formation: An LLM-based Cognitive Agent Approach with Deep Reinforcement Learning

Abstract Traditional neoclassical labor market models, predicated on perfect competition, complete information, and full rationality, fail to capture real-world complexities—including information asymmetry, bounded rationality, and dynamic skill mismatches—resulting in inadequate explanations of structural unemployment and inefficient job matching. To address this, the study develops an integrated simulation framework combining Large Language Models (LLMs), Deep Reinforcement Learning (DRL), and agent-based modeling: LLMs process job descriptions/resumes for multi-modal skill representation and belief updating; DRL models game-theoretic interactions between firms (optimizing hiring/salary) and job seekers (optimizing job search/negotiation) under bounded rationality; additional modules include policy-macroeconomy coupling (for taxes/subsidies/minimum wage) and skill evolution networks (for skill obsolescence/emergence). Experiments, calibrated to U.S. BLS/JOLTS data, yield concrete results: (1) A 10% hiring subsidy reduced unemployment to 4.5% and matching efficiency to 80%, outperforming other policies; (2) A 5% corporate tax increase raised unemployment to 8% and lowered vacancy rates to 3.5%; (3) Low-skill automation shocks pushed unemployment to 9% due to persistent skill mismatches. The framework accurately replicates dynamic labor market behaviors, outperforming traditional models in capturing policy heterogeneity and real-world inefficiencies.