This chapter focuses on stated preferences obtained from discrete choice experiments (DCEs also known as SPDCEs), as opposed to data that reflect real market choices (revealed preferences (RP) as discussed in Chapter 9). DCEs try to simulate the essential elements of real market options that consumers might face in the future. Unlike real market choice data, DCEs rely on constructed markets in ...

Recent Monte Carlo work on choosing experimental designs for discrete choice experiments seemed to greatly simplify this choice for applied researchers. It suggested that (a) commonly used designs can generate unbiased estimates for indirect utility function specifications with main effects only and main effects plus higher order terms, and (b) random designs are more efficient than main effect...

We consider an environment in which agents face various choice sets, assembled from a finite universe of objects, and choose a single object each time a choice set is presented to them. Models for probabilistic discrete choice give, for each choice set, a discrete probability distribution over that choice set. We use methods of Bayesian model comparison to measure the empirical plausibility of ...

We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an adaptive discrete-choice questionnaire. Second, integrate the estimated part-wo...

The allocation of combinations of attribute levels to choice situations in stated choice (SC) experiments can have a significant influence upon the resulting study outputs once data is collected. Recently, a small but growing stream of research has looked at using what have become known as efficient SC experimental designs to allocate the attribute levels to choice situations in a manner design...