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While simulations have been utilized in diverse domains, such as urban growthmodeling, market dynamics modeling, etc; some of these applications may requirevalidations based upon some real-world observations modeled in the simulation,as well. This validation has been categorized into either qualitativeface-validation or quantitative empirical validation, but as the importance andthe accumulation of data grows, the importance of the quantitative validationhas been highlighted in the recent studies, i.e. digital twin. The keycomponent of quantitative validation is finding a calibrated set of parametersto regenerate the real-world observations with simulation models. While thisparameter calibration has been fixed throughout a simulation execution, thispaper expands the static parameter calibration in two dimensions: dynamiccalibration and heterogeneous calibration. First, dynamic calibration changesthe parameter values over the simulation period by reflecting the simulationoutput trend. Second, heterogeneous calibration changes the parameter valuesper simulated entity clusters by considering the similarities of entity states.We experimented the suggested calibrations on one hypothetical case and anotherreal-world case. As a hypothetical scenario, we use the Wealth DistributionModel to illustrate how our calibration works. As a real-world scenario, weselected Real Estate Market Model because of three reasons. First, the modelshave heterogeneous entities as being agent-based models; second, they areeconomic models with real-world trends over time; and third, they areapplicable to the real-world scenarios where we can gather validation data.

Automatic Calibration of Dynamic and Heterogeneous Parameters in Agent-based Model