Decision Forest: A Nonparametric Approach to Modeling Irrational Choice

67 Pages Posted: 7 May 2019 Last revised: 29 Jun 2021

See all articles by Yi-Chun Chen

Yi-Chun Chen

University of California, Los Angeles (UCLA) - Anderson School of Management

Velibor Mišić

University of California, Los Angeles (UCLA) - Anderson School of Management

Date Written: May 18, 2020

Abstract

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 non-rational behavior, we show that the model outperforms both rational and non-rational benchmark models in out-of-sample predictive ability.

Keywords: nonparametric choice modeling, decision trees, non-rational behavior, linear optimization

Suggested Citation

Chen, Yi-Chun and Misic, Velibor, Decision Forest: A Nonparametric Approach to Modeling Irrational Choice (May 18, 2020). Available at SSRN: https://ssrn.com/abstract=3376273 or http://dx.doi.org/10.2139/ssrn.3376273

Yi-Chun Chen

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Velibor Misic (Contact Author)

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

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