Demand Estimation and Forecasting Using Cognitive Models of Consumer Choice

37 Pages Posted: 14 Jun 2019 Last revised: 1 Jun 2021

See all articles by Nan Chen

Nan Chen

National University of Singapore (NUS) - School of Computing

John A. Clithero

Lundquist College of Business, University of Oregon

Ming Hsu

University of California, Berkeley - Haas School of Business

Date Written: May 31, 2021

Abstract

Empirical studies of consumer choice increasingly make use of cognitive process models that incorporate behavioral insights while maintaining quantitative rigor. At the same time, important questions remain regarding their ability to deliver insights that are at once accurate, generalizable, and managerially relevant. We take a step toward addressing these issues by conducting a series of consumer choice experiments and comparing the accuracy and generalizability of the canonical multinomial logit model (MNL) against the drift diffusion model (DDM), a cognitive model that incorporates response times in addition to choice data. We find that the DDM robustly outperforms MNL in providing accurate forecasts on several managerially relevant measures, and that these improvements generalize to out-of-sample scenarios involving new consumers and new choice environments. Perhaps most impressively, relative performance improvements when generalizing across individuals are greater in magnitude than when using holdout choices within the same individuals. Additional analyses further show that these improvements derive from the ability of the cognitive model to capture heterogeneity in the tradeoff between time spent on deliberation and the probability of mistakenly choosing a lower-valued option. We conclude with a discussion of the implications of these findings for theoretical and applied work in consumer choice modeling.

Keywords: Cognitive modeling, drift diffusion model, demand estimation, response times, out-of-sample, generalizability

Suggested Citation

Chen, Nan and Clithero, John A. and Hsu, Ming, Demand Estimation and Forecasting Using Cognitive Models of Consumer Choice (May 31, 2021). Available at SSRN: https://ssrn.com/abstract=3397895 or http://dx.doi.org/10.2139/ssrn.3397895

Nan Chen

National University of Singapore (NUS) - School of Computing ( email )

13 Computing Drive
Computing 1
Singapore 117543, 117417
Singapore

HOME PAGE: http://sites.google.com/site/ttnanchen

John A. Clithero (Contact Author)

Lundquist College of Business, University of Oregon ( email )

Lundquist College of Business
1208 University of Oregon
Eugene, OR 97403
United States

Ming Hsu

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

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