Introduction

Simulation gives the researcher the freedom to specify models that appropriately represent the choice situations under consideration, without being unduly hampered by purely mathematical concerns. This perspective has been the overarching theme of our book. The discrete choice models that we have discussed – namely, logit, nested logit, probit, and mixed logit – are used in the vast majority of applied work. However, readers should not feel themselves constrained to use these models. In the current chapter, we describe several models that are derived under somewhat different behavioral concepts. These models are variations on the ones already discussed, directed toward specific issues and data. The point is not simply to describe additional models. Rather, the discussion illustrates how the researcher might examine a choice situation and develop a model and estimation procedure that seem appropriate for that particular situation, drawing from, and yet adapting, the standard set of models and tools.

Each section of this chapter is motivated by a type of data, representing the outcome of a particular choice process. The arena in which such data might arise is described, and the limitations of the primary models for these data are identified. In each case, a new model is described that better represents the choice situation. Often this new model is only a slight change from one of the primary models. However, the slight change will often make the standard software unusable, so that the researcher will need to develop her own software, perhaps by modifying the codes that are available for standard models.