Calibration

There are two main approaches to calibration:

a. Categorical calibration: researchers search for parameter values that produce model results within a predefined category or range of acceptability. s.

b. Best-Fit Calibration: In best-fit calibration, researchers aim to identify a single set of parameter values that cause the model to best match specific criteria or targets.

Calibration serves several purposes:

Matching Empirical Data: The primary purpose of calibration is to ensure that the model's outputs closely match observed empirical data. By adjusting the parameter values, researchers aim to minimize the discrepancies between the model's predictions and the real-world data.

Estimating Unmeasured Parameters: Calibration helps estimate the values of parameters that cannot be directly measured or evaluated. By comparing the model's outputs with available data, researchers can infer the likely values of these parameters, improving the model's accuracy and realism.

Testing Structural Realism: Calibration provides an opportunity to assess the model's structural realism. By calibrating the model to match observations within a reasonable range, researchers can evaluate if the model captures the essential characteristics and dynamics of the real-world phenomenon.