As datasets capturing human choices grow in richness and scale—particularly in online domains—there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce’s choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not ...

This paper studies a Stated Preference (SP) experiment on the choice of type of (Rail) season card, conducted among current Dutch Railways season cardholders. They were asked to choose from the following three alternatives: (1) an unrestricted season card, (2) a cheaper season card with peak travel and travel frequency restrictions, and (3) not buying a season card. Multinomial logit (MNL), nes...

This paper presents the process of derivation and development of a spatial multinomial logit model and its application to a housing type choice problem. Over the past few years, a relatively small body of research was developed that tries to capture the spatial and temporal dependencies across decision-makers and alternatives. While temporal dependencies are often considered especially in dynam...

Market demand estimation is an important process to assess the financial feasibility of new product development (NPD) projects. The development of models for market demand estimation involves market potential estimation and choice modeling. Previous studies commonly used conjoint analysis to develop utility functions which were then used in discrete choice models to generate market share models...

In many implemented network revenue management systems, a bid price control is being used. In this form of control bid, prices are attached to resources, and a product is offered if the revenue derived from it exceeds the sum of the bid prices of its consumed resources. This approach is appealing because once bid prices have been determined, it is fairly simple to derive the products that shoul...

In this note, we take up a computational problem observed with fitting such mixtures of discrete choice models as the mixed multinomial logit, the parameter values explode as a numerical optimization algorithm maximizes the logarithm of the simulated likelihood function. We describe two identification issues that can increase the probability of this phenomenon. First, the parameters of the vari...

This paper deals with the correspondence between macro and micro spatial interaction and choice models, in both a static and a dynamic context. After a review of recent literature, special attention is devoted to the interpretation of aggregate spatial interaction models (both the conventional gravity or entropy models and the more general theory of movement devëloped by Alonso) in the framewor...

In conventional random-utility models, such as the multinomial logit model, it is assumed that a decisionmaker's choice set is independent of his or her preferences conditional on the explanatory variables of the models. However, there are many situations in which the decisionmaker chooses the choice set, thereby making the independence assumption implausible. For example, if information about ...

Unique parametrizations of models are very important for parameter interpretation and consistency of estimators. In this paper we analyze the identifiability of a general class of finite mixtures of multinomial logits with varying and fixed effects, which includes the popular multinomial logit and conditional logit models. The application of the general identifiability conditions is demonstrate...

We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification ...