Dynamic labor market matching via deep reinforcement learning: continuous skill taxonomy evolution in technological transitions

Purpose Rapid digitalization, automation and AI are widening skill mismatches and slowing hiring, and depressing productivity overall. We propose and evaluate an adaptive labor-market matching framework that treats the skill taxonomy as a living, reinforcement-learning-controlled graph, continuously updated from labor-market signals to improve matching quality and time-to-hire. Design/methodology/approach The system integrates labor-economics mechanisms with modern AI. A multilingual transformer pipeline extracts skills from unstructured worker profiles and job ads; a graph neural network evolves the taxonomy; and a multi-agent reinforcement-learning layer optimizes matching policies while a feedback module aligns ranking signals with long-horizon outcomes (productivity and tenure). Evidence combines a longitudinal corpus spanning 2010–2025 across 18 countries (1.2 M worker profiles; 8.7 M job postings) with stress-test simulations against shocks and emerging skills. Findings The framework improves Precision@1 by 9.4% over the best baseline, reduces time-to-hire by 37.2% versus static taxonomies (16.5% versus the best learning baseline), and attains 76.3% Precision@1 on post-2020 emerging skills. Dynamic-taxonomy metrics show higher coverage (0.942), relationship accuracy (0.876), and ∼3.2 × faster incorporation of new skills. Economic outcomes include a 42.8% reduction in mismatch, a 28.7% post-placement productivity gain, and a 20.9% increase in tenure; counterfactual simulations project 5.4–7.8 percentage-point reductions in structural unemployment and 6.8–10.5% productivity gains under realistic adoption. Originality/value We operationalize a continuously learning skill taxonomy embedded in matching policy via multi-agent reinforcement learning, bridging labor economics and AI. The approach is deployable within existing ATS/PES/EdTech workflows with governance. Limitations (uneven data coverage and interpretability) motivate integration with education systems and expanded fairness auditing.