Elsevier

Theoretical Computer Science

Volume 808, 12 February 2020, Pages 99-107
Theoretical Computer Science

Parameter identification in Markov chain choice models

https://doi.org/10.1016/j.tcs.2019.11.014Get rights and content
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Abstract

This work studies the parameter identification problem for the Markov chain choice model of Blanchet, Gallego, and Goyal used in assortment planning. In this model, the product selected by a customer is determined by a Markov chain over the products, where the products in the offered assortment are absorbing states. The underlying parameters of the model were previously shown to be identifiable from the choice probabilities for the all-products assortment, together with choice probabilities for assortments of all-but-one products. Obtaining and estimating choice probabilities for such large assortments is not desirable in many settings. The main result of this work is that the parameters may be identified from assortments of sizes two and three, regardless of the total number of products. The result is obtained via a simple and efficient parameter recovery algorithm.

Keywords

Discrete choice models
Markov chain choice models
Parameter identification

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