An ACP-Based Parallel Economic System Framework for Economic Analysis

Economic analysis plays a vital role in policy-making and economic optimization. Traditional economic analysis methods rely on predefined models to fit economic operation data and simulate the economic evolution process, but these methods suffer from insufficient flexibility and scalability. To address these challenges, we propose a parallel economic system based on the ACP (Artificial systems, Computational experiments, Parallel execution) approach. Specifically, we develop three types of intelligent agents—government, enterprise, and resident—based on the real business cycle model. Using these agents, an artificial economic system is created, capable of generating extensive data for diverse scenarios with various goals and parameters. Computational experiments are then conducted to analyze the generated data, learning optimal solutions for different scenarios. Finally, the learned solutions are applied to reveal economic laws and optimize physical economic systems through parallel execution. The proposed parallel economic system can accurately simulate economic activities under different tax rates and subsidy conditions, providing effective support for decision-makers in formulating economic adjustment policies.