Accounting for model uncertainty in individualized designs for discrete choice experiments

Guido Consonni, Laura Deldossi, Eleonora Saggini

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider the issue of finding optimal designs for Discrete Choice Experiments (DCE). Currently the optimal design for a DCE is predicated on a specific mixed logit model which represents the probability of choosing a specific alternative in a choice set through a linear predictor combining several factors. Using a unique model represents a major limitation which we overcome by allowing for a collection of different models characterized by distinct linear predictors. In this new setting, model choice becomes a major concern. By maximizing the utility of selecting a choice set - computed as the mutual information between the model indicator and the predicted observation at that set - over alternative choice sets, model discrimination is enhanced. We implement our methodology using a sequential Monte Carlo algorithm suitably tailored to deal with model uncertainty.
Original languageEnglish
Title of host publicationCladag 2017. Book of short papers
Publication statusPublished - 2017
EventCLADAG 2017 - Milano
Duration: 13 Sep 201715 Sep 2017


ConferenceCLADAG 2017


  • Bayesian sequential design
  • Model discrimination
  • Optimal design


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