This research focuses on the numerical computation and simulation of macroeconomic system operating laws, overcoming the inherent limitations of traditional empirical analysis, static statistics, and lagging assessment. Drawing on digital twin modeling, multi-agent simulation, and complex-system quantification methods, it systematically deconstructs the operating mechanisms, factor transmission pathways, and dynamic evolution characteristics of macroeconomic systems. Macroeconomics, as a typical open complex system, exhibits dynamic iteration, multi-agent interaction, and cross-domain linkage. Conventional paradigms that rely on historical time-series data and fixed models struggle to adapt precisely to complex scenarios where policy adjustments, market volatility, and external shocks interact. The research adopts holographic simulation and projection as its core technical approach, precisely depicting the coupled relationships among industry, finance, policy, and market dimensions, quantifying the disturbance effects and transmission differences of endogenous and exogenous variables on the economic system, and enabling implicit economic operating laws to become modelable, computable, reproducible, and projectable.

Grounded in a virtual-real symbiotic simulation logic, the research constructs a bidirectional synchronous mapping mechanism between the real economy and digital simulation entities, establishing a closed-loop research framework in which real-world data calibrates simulation models and simulation conclusions inform real-world assessment. By continuously iterating the simulation system with authentic economic time-series data, industrial microdata, and policy parameters, it effectively addresses the insufficient dynamic adaptability of traditional models. Through multi-scenario numerical simulation, multi-plan comparative computation, and systematic risk quantification testing, it comprehensively reconstructs the diverse pathways and uncertainty characteristics of economic system evolution, accurately measuring policy implementation effects, market risk transmission intensity, and industrial structure evolution trends, thereby providing scientific, quantifiable, and verifiable experimental means for research on economic operating mechanisms.

This research adopts mathematical computation, simulation modeling, and quantitative analysis as its core research paradigm, strictly adhering to the academic positioning of decision support and the research principles of human-machine collaboration, while upholding academic norms of traceable model conclusions, quantifiable errors, and openly disclosed assumptions. Grounded in authentic economic operating characteristics and objective data foundations, it empowers macroeconomic and industrial economic research through computational simulation technology, providing standardized, visualizable, and reusable quantitative research tools for academic research and professional practice in fields such as macro governance analysis, industrial trend assessment, and policy effect evaluation. Through ongoing model iteration and mechanism validation, this research aims to refine the simulation research system for complex economic systems, provide reliable scientific support for scholars and research practitioners in related fields, and advance the digital and refined upgrading of macroeconomic research paradigms.