Discrete Choice Modeling

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The field models choices among options in settings such as travel, retail, healthcare, and online rankings, to explain preferences and predict decisions. Core approaches include random-utility models such as multinomial logit and probit, Plackett–Luce ranking models, mixtures, and panel models; these also support tasks like assortment design and choice-based experimentation. Neural networks, graph models, and foundation models are increasingly used to capture complex interactions and individual differences, sometimes alongside reinforcement learning for model selection or sequential decisions. A key tension is improving prediction with flexible methods while preserving interpretable preferences, economic constraints, reliable uncertainty estimates, and robustness to changing choice sets or environments.

Latest papers

44 papers10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Boundedly Rational Meta-Learning in Sequential Consumer Choice
May 15, 2026Mehrzad Khosravi, Max Kleiman-Weiner, Hema YoganarasimhanMeta-Learning Sequential Decision Making
University of Washington
Contextual Plackett-Luce: An Efficient Neural Model for Probabilistic Sequence Selection under Ambiguity
May 9, 2026Noam Mizrachi, Nadav Har-Tuv, Shai Shalev-ShwartzDiscrete Choice Modeling Structured Prediction
Mobileye
Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models
May 7, 2026Shivaram Subramanian, Zhengliang Xue, Markus Ettl +2Large Language Model-Guided Optimization Discrete Choice Modeling
IBM T.J. Watson Research Center Yorktown Heights, New York, 10598, U.S.A.
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
Mar 25, 2026Easton Huch, Michael KeaneNeural Surrogate Modeling Amortized Inference
Postdoctoral Fellow, Johns Hopkins Carey Business School · Carey Distinguished Professor, Johns Hopkins Carey Business School
Contextual Preference Distribution Learning
Mar 17, 2026Benjamin Hudson, Laurent Charlin, Emma FrejingerPreference Learning Decision-Making under Uncertainty
Mila – Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada · Département d’informatique et de recherche opérationnelle (DIRO), Université de Montréal, Montreal, Quebec, Canada · HEC Montréal, Montreal, Quebec, Canada
Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects
Mar 11, 2026Kimia Kamal, Bilal FarooqCausal Inference with Unobserved Confounding Causal Inference
Laboratory of Innovations in Transportation (LiTrans), Toronto Metropolitan University, Toronto, Canada
Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis
Mar 6, 2026Bin Xuan, Jungmin Hwang, Hakyeon LeePreference Elicitation Digital Twins
Department of Data Science Seoul National University of Science and Technology Seoul, Republic of Korea · Department of Industrial Engineering Seoul National University of Science and Technology Seoul, Republic of Korea
From GEV to ResLogit: Spatially Correlated Discrete Choice Models for Pedestrian Movement Prediction
Mar 1, 2026Rulla Al-Haideri, Bilal FarooqDiscrete Choice Modeling Pedestrian Trajectory Prediction
Laboratory of Innovations in Transportation (LiTrans), Toronto Metropolitan University, 350 Victoria Street, Toronto, Ontario, Canada
Learning Preference from Observed Rankings
Feb 18, 2026Yu-Chang Chen, Chen Chian Fuh, Shang En TsaiPreference Modeling User Preference Modeling
National Taiwan University
Alternative Graph Neural Networks: Synergizing GEV Models and Deep Learning for Travel Mode Choice Modeling
Sep 8, 2025Yuqi Zhou, Zhanhong Cheng, Dingyi Zhuang +3Graph Neural Networks Discrete Choice Modeling
aDepartment of Urban and Regional Planning, University of Florida · dDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology · bCollege of Architecture, Texas A&M University +1
Delphos: A reinforcement learning framework for assisting discrete choice model specification
Jun 6, 2025Gabriel Nova, Stephane Hess, Sander van CranenburghDiscrete Choice Modeling Model Selection
CityAI Lab, Transport and Logistics Group, Delft University of Technology, The Netherlands · Choice Modelling Centre - Institute for Transport Studies, University of Leeds, England
Bayesian Deep Learning for Discrete Choice
May 23, 2025Daniel F. Villarraga, Ricardo A. DazianoBayesian Neural Networks Bayesian Inference
School of Civil and Environmental Engineering, Cornell University, 220 Hollister Hall, Ithaca, NY 14853, USA
Pairwise Comparisons without Stochastic Transitivity: Model, Theory and Applications
Jan 13, 2025Sze Ming Lee, Yunxiao ChenPairwise Comparison Pairwise Preference Evaluation
Department of Statistics · London School of Economics and Political Science · London WC2A 2AE, United Kingdom
The EM-algorithm and the Method of Moments in Softmax Mixture Models
Sep 16, 2024Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1Expectation-Maximization Discrete Choice Modeling
Department of Statistical Sciences, University of Toronto · Department of Statistics and Data Science, Cornell University · Courant Institute of Mathematical Sciences and Center for Data Science, New York University +1

May 15, 2026J/K move · Enter open · S save

Boundedly Rational Meta-Learning in Sequential Consumer Choice

Mehrzad Khosravi, Max Kleiman-Weiner, Hema Yoganarasimhan

University of Washington

Meta-Learning Sequential Decision Making Discrete Choice Modeling Bayesian Decision Theory Bounded Rationality Decision-Making under Uncertainty

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Many consumer decisions are repeated choices under uncertainty. Standard models capture these decisions using Bayesian learning and dynamic programming: consumers update beliefs from feedback and use those beliefs to guide future choices. In many markets, however, learning does not restart when consumers enter a new context: prior experience with a brand, product, or provider can shape beliefs in later, related decisions. We study this cross-context knowledge transfer, or meta-learning, in sequential choice. We design a hierarchical laboratory task in which participants repeatedly choose among airlines across routes and observe noisy binary outcomes. Reduced-form evidence shows that participants improve not only within routes, but also across routes: they choose better airlines earlier in later routes and reduce pseudo-regret. To identify the mechanism behind this transfer, we compare human choices to a no-transfer benchmark and a fully integrated Bayesian meta-learning benchmark. In particular, we introduce a class of boundedly rational meta dynamic programming policies, BRMDP(D), that approximate full integration using a limited number of hyper-posterior draws, denoted by D. Trial-by-trial likelihood comparisons show that low-D boundedly rational meta-learning, especially BRMDP(1), fits participant behavior better than both no transfer and fully integrated Bayesian transfer. Consumers, therefore, transfer brand-level regularities across contexts, but through coarse representations of prior uncertainty. The findings imply that models of consumer learning should allow for approximate cross-context transfer, and that managerial counterfactuals based on either no-transfer or fully integrated learning can be misleading.