Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model

We study assortment and price optimization under the contextual concavity (CC) model introduced in the literature, which subsumes the well-known multiattribute loss aversion (MLA) model. Unlike context-independent choice models that assume product utilities are unaffected by other alternatives in the assortment, the CC model offers a context-dependent framework that incorporates reference points across multiple attributes and captures prominent context effects (e.g., the compromise effect) well documented in the empirical literature. We analytically characterize the structure of the optimal assortment in several settings and show that the pure assortment problem under the CC model can be reformulated as a mixed-integer linear program (MILP) that is polynomial in the number of products for a fixed number of attributes. For the joint assortment and pricing problem, we prove that the optimal assortment consists of all products, derive the structure of the optimal prices, and develop an approximation algorithm for computing a near-optimal solution. Finally, using MNL as a stylized benchmark, we conduct numerical experiments that provide illustrative evidence of how ignoring context effects may affect assortment decisions and profitability.

Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0377.