Designing Pareto optimal stimuli for multiattribute choice experiments

Full factorial designs have long been used in designing multiattribute stimuli (e.g., hypothetical job applicants) for use in policy capturing and functional measurement models. More recently, marketing researchers have employedfractional factorial designs in multiatribute preference models, such as those used in conjoint analysis.Occasions arise where the researcher also desires the stimulus profiles to be Pareto optimal. This paper addresses some conceptual and methodological issues associated with Pareto optimal choice sets. In particular, we discuss the problem of determining the expected number of dominant-entry pairs. We then consider the task of deriving Pareto optimal choice sets from fractional factorial designs. A heuristic for accomplishing this is described and applied to an illustrative set of main effects and main effects plus interactions designs.