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

• 成果类型: Article
• 署名作者: Najafi, Sajjad; Jasin, Stefanus; Uichanco, Joline; Zhao, Jinglong
• 署名单位: Hautes Etudes Commerciales (HEC) Paris; University of Michigan System; University of Michigan; New York University; New York University Tandon School of Engineering; Boston University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2023.0377
• 发表日期: 2026
• 关键词: assortment optimization; pricing; choice models; contextual effects; loss aversion; Consumer choice; prospect-theory; ALTERNATIVES; management

摘要

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 mixedinteger 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.