Parameterizing bayesian network representations of social-behavioral models by expert elicitation

Bayesian networks provide a general framework with which to model many natural phenomena. The mathematical nature of Bayesian networks enables a plethora of model validation and calibration techniques: e.g. parameter learning, structure learning, goodness of fit tests, and diagnostic checking of the model assumptions. However, they are not free of shortcomings. With regard to parameter learning, in practice it is not uncommon to find oneself lacking adequate data to reliably estimate all model parameters. In this paper we present the early development of a novel application of conjoint analysis as a method for eliciting and modeling expert opinions and for using the results in a methodology for calibrating the parameters of a Bayesian network.