The Non-Markovian Nature of Nested Logit Choice

Discrete choice models are widely used for understanding how customers choose between a variety of substitutable goods. We investigate the relationship between two well studied choice models, the Nested Logit (NL) model and the Markov choice model. Both models generalize the classic Multinomial Logit model and admit tractable algorithms for assortment optimization. Previous evidence indicates that the NL model may be well approximated by, or be a special case of, the Markov model. We establish that the Nested Logit model, in general, cannot be represented by a Markov model. Further, we show that there exists a family of instances of the NL model where the choice probabilities cannot be approximated to within a constant error by any Markov choice model