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.

翻译：我们提出一种新的离散选择模型，称为广义随机偏好（GSP）模型，该模型将非理性因素引入随机偏好（SP）选择模型（亦称基于排序的选择模型）。该模型能够解释若干SP模型无法表征的选择现象（如折中效应与吸引效应），同时依然涵盖SP模型类。GSP模型被定义为消费者类型的分布，其中每种类型扩展了SP模型中理性类型的选择行为。基于现有SP模型估计方法，我们提出针对GSP模型的迭代估计算法，通过每轮迭代求解整数线性规划以发现新的类型。进一步证明，我们所提出的非理性概念可被纳入其他选择模型，例如随机效用最大化（RUM）模型类及其任意子类。作为具体实例，我们引入经典MNL模型的非理性扩展（称为广义MNL模型，即GMNL模型），并提出用于估计GMNL模型的高效期望最大化（EM）算法。基于真实选择数据的数值评估表明，GMNL与GSP模型在样本外预测精度上优于其理性对应模型。