Discrete Choice Modeling

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Logic-Logit: A Logic-Based Approach to Choice Modeling
Oct 4, 2026Shuhan Zhang, Wendi Ren, Shuang LiHuman Preference Modeling Interpretable ML
CUHK-Shenzhen
Consideration Circuits: Depth Separation and Universality Beyond a Single Softmax
Oct 2, 2026Junjie Xiao, Huiwen JiaDiscrete Choice Modeling
Department of Mathematics, Peking University · Department of Industrial Engineering and Operations Research, University of California, Berkeley
Multitask Reinforcement Learning for Assisting Choice Model Specification
Sep 16, 2026Gabriel Nova, Stephane Hess, Sander Van CranenburghDiscrete Choice Modeling Deep Sets
CityAI Lab, Transport and Logistics Group, Delft University of Technology, The Netherlands · Institute for Transport Studies and Choice Modelling Centre, University of Leeds, UK
MoPLEx: Estimating Plackett-Luce Mixture Models for Multi-Objective Alignment
Aug 25, 2026Dongyue Li, Ziniu Zhang, Lu Wang +1Learning to Rank Discrete Choice Modeling
Northeastern University, Boston, MA · University of Michigan, Ann Arbor, MI
Demand Transfer Estimation at Scale via Restricted Logit Modeling
Aug 13, 2026Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav +4Discrete Choice Modeling
Walmart Global Tech, Sunnyvale, CA, USA
An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks
Jul 30, 2026Sheng Lun Christine Cao, Destenie Nock, Alex DavisDiscrete Choice Modeling User Preference Modeling
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada · Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA, USA +1
Tabular Foundation Models for Discrete Choice Estimation
Jul 14, 2026Liu Liu, Dan ZhangTabular Foundation Models Discrete Choice Modeling
Leeds School of Business, University of Colorado Boulder
Diversified Multinomial Logit Contextual Bandits
Jul 13, 2026Heesang Ann, Taehyun Hwang, Min-hwan OhMulti-Armed Bandits Discrete Choice Modeling
Seoul National University
Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data
Jul 10, 2026Yalcin Akcay, Gerardo Berbeglia, Young-San LinExpectation-Maximization Discrete Choice Modeling
Melbourne Business School
Sensitivity to Subjective Expected Utility Maximization: A Methodological Study, with an Illustrative Application to LLM Decision-Making
Jul 8, 2026Jeff HelznerParameter Estimation Parameter Identifiability
OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction
Jul 3, 2026Changjian Liu, Yong Gao, Yuqing Wang +5Distribution Shift Robustness Discrete Choice Modeling
School of Earth and Space Sciences Peking University China, CN 100871 · College of Artificial Intelligence Zhejiang University China, CN 310027
Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees
Jun 24, 2026Yingshuo Wang, Xian Sun, Yanhang Li +2Tabular Foundation Models Discrete Choice Modeling
University of California, Berkeley, CA, USA. · Duke University, Durham, NC, USA. · Northeastern University, Boston, MA, USA. +2
A Minimal Model of Bounded Trade-Off Screening in Multi-Attribute Choice
Jun 11, 2026Manisha Dubey, Anirban Sarkar, Subramanian RamamoorthyComputational Cognitive Modeling Multi-Criteria Decision Analysis
School of Informatics, University of Edinburgh, UK · Cold Spring Harbor Laboratory, USA
Worker Utility as Hysteresis: A Preisach Model of Transaction Acceptance in Gig Labour Markets
Jun 3, 2026Piotr FrydrychDiscrete Choice Modeling AI and Labor
Metrology and Biomedical Engineering Institute, Faculty of Mechatronics, Warsaw University of Technology, 00-661 Warsaw, Poland
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
May 26, 2026Yingshuo Wang, Xian Sun, Yanhang Li +2Tabular Foundation Models Discrete Choice Modeling
University of California, Berkeley, CA, USA · Duke University, Durham, NC, USA · Northeastern University, Boston, MA, USA +2
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
May 25, 2026Joongkyu Lee, Min-hwan OhMulti-Armed Bandits Discrete Choice Modeling
Seoul National University, Seoul, Korea.
Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
May 17, 2026Xi Chen, Shibo Dai, Jiameng Lyu +1Multi-Armed Bandits Discrete Choice Modeling
Leonard N. Stern School of Business, New York University, New York, NY 10012, USA · Qiuzhen College, Tsinghua University, Beijing 100084, China · Department of Management Science, School of Management, Fudan University, Shanghai, 200433, China +1
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
Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
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
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
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

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Logic-Logit: A Logic-Based Approach to Choice Modeling

Shuhan Zhang, Wendi Ren, Shuang Li

CUHK-Shenzhen

Human Preference Modeling Interpretable ML Discrete Choice Modeling

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In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice models have been widely applied across various domains---such as commercial demand forecasting, recommendation systems, and consumer behavior analysis---typically categorized as parametric, nonparametric, or deep network-based. While recent innovations have favored neural network approaches for their computational power, these flexible models often involve large parameter sets and lack interpretability, limiting their effectiveness in contexts where transparency is essential. Previous empirical evidence shows that individuals usually use heuristic decision rules to form their consideration sets, from which they then choose. These rules are often represented as disjunctions of conjunctions (i.e., OR-of-ANDs). These rules-driven, consider-then-choose decision processes enable people to quickly screen numerous alternatives while reducing cognitive and search costs. Motivated by this insight, our approach leverages logic rules to elucidate human choices, providing a fresh perspective on preference modeling. We introduce a unique combination of column generation techniques and the Frank-Wolfe algorithm to facilitate efficient rule extraction for preference modeling---a process recognized as NP-hard. Our empirical evaluation, conducted on both synthetic datasets and real-world data from commercial and healthcare domains, demonstrates that Logic-Logit significantly outperforms baseline models in terms of interpretability and accuracy.

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