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The global economy is one of today's major challenges, with increasingrelevance in recent decades. A frequent observation by policy makers is thelack of tools that help at least to understand, if not predict, economiccrises. Currently, macroeconomic modeling is dominated by Dynamic StochasticGeneral Equilibrium (DSGE) models. The limitations of DSGE in coping with thecomplexity of today's global economy are often recognized and are the subjectof intense research to find possible solutions. As an alternative or complementto DSGE, the last two decades have seen the rise of agent-based models (ABM).An attractive feature of ABM is that it can model very complex systems becauseit is a bottom-up approach that can describe the specific behavior ofheterogeneous agents. The main obstacle, however, is the large number ofparameters that need to be known or calibrated. To enable the use of ABM withdata from the real-world economy, this paper describes an agent-basedmacroeconomic modeling approach that can read a Social Accounting Matrix (SAM)and deploy from scratch an economic system (labor, activity sectors operatingas firms, a central bank, the government, external sectors...) whose structureand activity produce a SAM with values very close to those of the actual SAMsnapshot. This approach paves the way for unleashing the expected highperformance of ABM models to deal with the complexities of current globalmacroeconomics, including other layers of interest like ecology, epidemiology,or social networks among others.

An agent-based modeling approach for real-world economic systems: Example and calibration with a Social Accounting Matrix of Spain