Decision Forest: A Nonparametric Approach to Modeling Irrational Choice

• 成果类型: Article
• 署名作者: Chen, Yi-Chun; Misic, Velibor V.
• 署名单位: University of California System; University of California Los Angeles
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4256
• 发表日期: 2022
• 页码: 7090-7111
• 关键词: nonparametric choice modeling; Decision Trees; nonrational behavior; linear optimization

摘要

Customer behavior is often assumed to follow weak rationality, which implies that adding a product to an assortment will not increase the choice probability of another product in that assortment. However, an increasing amount of research has revealed that customers are not necessarily rational when making decisions. In this paper, we propose a new nonparametric choice model that relaxes this assumption and can model a wider range of customer behavior, such as decoy effects between products. In this model, each customer type is associated with a binary decision tree, which represents a decision process for making a purchase based on checking for the existence of specific products in the assortment. Together with a probability distribution over customer types, we show that the resulting model-a decision forest-is able to represent any customer choice model, including models that are inconsistent with weak rationality. We theoretically characterize the depth of the forest needed to fit a data set of historical assortments and prove that with high probability, a forest whose depth scales logarithmically in the number of assortments is sufficient to fit most data sets. We also propose two practical algorithms-one based on column generation and one based on random sampling-for estimating such models from data. Using synthetic data and real transaction data exhibiting nonrational behavior, we show that the model outperforms both rational and nonrational benchmark models in out-of-sample predictive ability.