How to validate and calibrate agent-based models?
The terms validation and calibration originate in engineering and are sensitive to many social scientists. What we want to test is how well our model explains the data compared with alternative models. Therefore it is important to use different models of decision making. Human decision making is so complicated that we do not have the illusion to argue that we have the true model. Model comparison is, however, not a straightforward activity. There are different dimensions in which a model may be evaluated. One way social scientists test models is use maximum likelihood estimation. Since more complicated models with more degrees of freedom result in better fits, some scholars use a penalty for the complexity of the model. Especially the use of minimum description length might be a promising tool. Increasingly experimental researchers and agent-based modeling start to be combined. Traditionally experiments where used to challenge the standard model of rational choice, nowadays alternative
