Jose Blanchet, Guillermo Gallego, Vineet Goyal (2016) A Markov Chain Approximation to Choice Modeling. Operations Research 64(4):886-905. https://doi.org/10.1287/opre.2016.1505
Abstract
Assortment planning is an important problem that arises in many industries such as retailing and airlines. One of the key challenges in an assortment planning problem is to identify the “right” model for the substitution behavior of customers from the data. Error in model selection can lead to highly suboptimal decisions. In this paper, we consider a Markov chain based choice model and show that it provides a simultaneous approximation for all random utility based discrete choice models including the multinomial logit (MNL), the probit, the nested logit and mixtures of multinomial logit models. In the Markov chain model, substitution from one product to another is modeled as a state transition in the Markov chain. We show that the choice probabilities computed by the Markov chain based model are a good approximation to the true choice probabilities for any random utility based choice model under mild conditions …
Authors: Jose Blanchet, Guillermo Gallego, Vineet Goyal
Publication date: 2016/8
Journal: Operations Research
Volume: 64
Issue: 4
Pages: 886-905
Publisher: INFORMS
