June 12, 2020 @ Room 01.18, Kasteelpark 30, Heverlee, Belgium
Many products and services can be described as mixtures of ingredients. One example is concrete, made of cement, water, and sand. Choice modeling deals with the choices of a group of decision makers from a finite set of options, such as customers deciding whether to buy product A or B. Choice modeling can be combined with mixtures to determine what combinations of ingredient proportions people prefer. For example, a choice model can be used to determine what combination of sugar, flour, egg and chocolate proportions is preferable to a group of subjects in a study.
Choice models are non-linear, hence an optimal design for a choice experiment is a function of the unknown parameters. There are several ways to circumvent this problem, one of them being a Bayesian approach. This approach has the additional benefit that it gives researchers the opportunity to include prior knowledge in the form of a prior distribution. This is advantageous because researchers almost always possess information about the phenomena they are investigating, as scientific research is sequential.
The current state-of-the-art for Bayesian choice experiments with mixtures is restricted to D-optimality. However, since we typically want models that give good predictions, an I-optimal design is often more appropriate than a D-optimal design because it is focused on the prediction variance of the model. In this seminar, I will compare results concerning I-optimal designs for choice experiments with mixtures with results concerning D-optimal designs.