Multi-Strategy Market Dynamics Analysis: A Novel Framework for Agent-Based Economic Modeling with Reinforcement Learning

This paper presents a Multi-Strategy Market Dynamics Analysis (MSMDA) framework for agent-based economic modeling with reinforcement learning. The primary methodological contribution is an integrated strategy–stability–macro inference pipeline that links population-level strategy evolution to dynamic market stability and model-internal counterfactual policy analysis. The framework is organized into six analytical components: Strategy Temporal Pattern Recognition (STPR), Strategy Transition Detection and Analysis (STDA), Strategy-Macro Causality Analysis (SMCA), the Dynamic Market Stability Index (DMSI), the Adaptive Rationality Equilibrium (ARE), and the Information Asymmetry Propagation (IAP) metric. The method is evaluated within a simulation dataset comprising 447,129 records across four experimental scenarios, 1500 discrete time periods, and 200 heterogeneous firms governed by proximal policy optimization. Results show that competitive strategies dominate market emergence patterns at 60.8% of all observations and achieve superior average profitability of 28.07 monetary units per period, compared with (Formula presented.) for dumping strategies and 7.83 for market power strategies. The DMSI reveals a mean stability of 0.372 with standard deviation 0.097, peaking at 0.780 during strategic consolidation and collapsing to zero during a major demand shock. Within the simulated economy, doubly-robust counterfactual analysis projects a 28.4% GDP increase from a market power-to-competition intervention and a 31.2% increase under full ARE optimization at (Formula presented.). The ARE further identifies a Pareto-optimal market configuration that jointly maximizes per-firm profit at 229.82 monetary units per period and systemic stability at DMSI (Formula presented.), indicating that efficiency and resilience need not conflict in the calibrated simulation environment. To address time-series autocorrelation in bootstrap inference throughout the framework, we employ a moving block bootstrap with data-adaptive block length selection based on the spectral density at frequency zero, providing finite-sample confidence intervals for the reported test statistics and counterfactual projections.