Validating agent-based marketing models through conjoint analysis

Agent-based modelers in the field of marketing research have paid little attention to validation issues. This paper provides a definition of validation relevant for this community of modelers. On the basis of a history-friendly model for simulation calibration [Malerba, F., Nelson, R., Orsenigo, L., and Winter, S. (1999). ‘History-friendly’ models of industry evolution: the computer industry. Industrial and corporate change, 8(1), 3–40.], the authors demonstrate how conjoint analyses can be used to instantiate and calibrate an agent-based marketing model. Methods for model instantiation using conjoint partworths and model calibration using the conjoint first-choice rule are demonstrated. When the model matches the results of the first-choice rules for consumer preferences, the modeler can feel more confident that calibration has been achieved. When verification replicates stylized facts on a macro-level, the model is one step closer to validation. Because conjoint data results are meaningful on an individual level as well as on an aggregate level, this type of empirical data collection is ideal for agent-based marketing models.