Motivated by the successes of deep learning, we propose a class of neural network-based
discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) …

This survey examines the transformative impact of Large Language Models (LLMs) and AI-
driven systems across three critical and interconnected business domains: Finance …

We study the problem when a firm sets prices for products based on the transaction data,
that is, which product past customers chose from an assortment and what were the historical …

Efficiently managing inventories requires an accurate estimation of stockout costs. This
estimation is complicated by challenges in determining how to compensate consumers …

Random coefficient multinomial logit models are widely used to estimate customer
preferences from sales data. However, these estimation models can only allow for products …

Problem definition: In brick-and-mortar fashion retail stores, inventory stockouts are frequent.
When a specific size of a fashion product is out of stock, the unmet demand might not be …

In the fast-growing e-commerce industry, optimizing revenue management is of utmost
importance. Discrete choice models have widely been used to describe customer …

Understanding how consumers make choices is of paramount importance, as it offers
insights into consumer purchase behavior across multiple products, enables accurate …

We propose a new approach to estimating the random coefficient logit demand model for
differentiated products when the vector of market-product level shocks is sparse. Assuming …

A standard assumption in the fitting of unordered multinomial response models for J
mutually exclusive nominal categories, on cross-sectional or longitudinal data, is that the …