  We propose a new discrete choice model, called the generalized stochastic
preference (GSP) model, that incorporates non-rationality into the stochastic
preference (SP) choice model, also known as the rank- based choice model. Our
model can explain several choice phenomena that cannot be represented by any SP
model such as the compromise and attraction effects, but still subsumes the SP
model class. The GSP model is defined as a distribution over consumer types,
where each type extends the choice behavior of rational types in the SP model.
We build on existing methods for estimating the SP model and propose an
iterative estimation algorithm for the GSP model that finds new types by
solving a integer linear program in each iteration. We further show that our
proposed notion of non-rationality can be incorporated into other choice
models, like the random utility maximization (RUM) model class as well as any
of its subclasses. As a concrete example, we introduce the non-rational
extension of the classical MNL model, which we term the generalized MNL (GMNL)
model and present an efficient expectation-maximization (EM) algorithm for
estimating the GMNL model. Numerical evaluation on real choice data shows that
the GMNL and GSP models can outperform their rational counterparts in
out-of-sample prediction accuracy.
