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Validating Agent-based Marketing Models Using Conjoint Analysis

Validation issues have received little attention by agent-based modelers in marketing research. In this paper we provide a definition of validation relevant for this community of modelers. Using the foundation of a history-friendly model for simulation calibration (Malerba, et al., 1999), we demonstrate how conjoint analyses can be used to instantiate and calibrate an agent-based marketing model. Model instantiation can be accomplished using conjoint partworths, and the conjoint first-choice rule can be used to calibrate these initial model settings. When the model matches the results of the first-choice rules for consumer preferences, a modeler can feel more confident that calibration is complete. When verification replicates stylized facts on a macro-level, the model is one step closer to being validated. 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 using in agent-based marketing models.