arXiv:2606. 26432v1 Announce Type: new Abstract: Tabular foundation models achieve strong accuracy on choice prediction tasks, but their predictions often violate the economic logic those tasks require: raising a price can increase predicted demand, implied willingness-to-pay estimates are frequently negative or implausible, and unavailable alternatives receive nonzero probability.
By Yingshuo Wang, Xian Sun, Yanhang Li, Zhichao Fan, Zexin Zhuang
arXiv:2505. 18077v3 Announce Type: replace-cross Abstract: Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences.
By Daniel F. Villarraga, Ricardo A. Daziano
arXiv:2607. 28854v1 Announce Type: new Abstract: Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models.
By Sheng Lun Christine Cao, Destenie Nock, Alex Davis
arXiv:2603. 24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making.
By Easton Huch, Michael Keane
arXiv:2209. 04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data.
By Ningyuan Chen, Setareh Farajollahzadeh, Qingwei Jin, Fanni Shen, Guan Wang
arXiv:2608. 12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization.
By Lakshya Garg, Deep Narayan Mishra, Swapnil Yadav, Haoan Wang, Sujal Alugubelli, Karthik Kumaran, Anupriya Sharma
arXiv:2607. 09817v1 Announce Type: new Abstract: We propose a framework for the Markov chain (MC) choice model with panel data, including parameter estimation, personalized choice prediction, and personalized assortment optimization.
By Yalcin Akcay, Gerardo Berbeglia, Young-San Lin
arXiv:2606. 25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder.
By Daolang Huang, Zhuoyue Huang, Conor Hassan, Luigi Acerbi, Samuel Kaski, Tom Rainforth
arXiv:2608. 03142v1 Announce Type: cross Abstract: We study contextual dynamic pricing with arbitrary covariate sequences and bounded, possibly nonbinary purchase quantities.
By Xueping Gong, Zhuoluo Zhang, Zhaowei Miao, Jiheng Zhang
arXiv:2608. 04669v1 Announce Type: new Abstract: Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival must receive a decision immediately, and the long-run usage of every resource must stay within its capacity.
By Mohammadsaeed Haghi, Mahdi Salmani, Nima Kelidari
arXiv:2606. 06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail.
By Adam N. Elmachtoub, Hyemi Kim, Jonathan Y. Tan
arXiv:2607. 16168v1 Announce Type: new Abstract: Residential short-term load forecasting (STLF) is challenging because household demand is heterogeneous, temporally variable, and shaped by diverse behavioural routines.
By Ramin Soleimani, Andrea Visentin, Dirk Pesch