arXiv:2608. 16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design.
arXiv:2304. 14385v4 Announce Type: replace-cross Abstract: We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product.
arXiv:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
arXiv:2606. 11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting.
arXiv:2209. 04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data.
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
arXiv:2606. 06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail.
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
arXiv:2606. 02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night).
arXiv:2510. 19119v2 Announce Type: replace Abstract: In networked environments, it is common for users to share recommendations about content, products, services, and possible courses of action.
We study repeated bilateral trade from a fairness perspective. At each round, a fresh seller-buyer pair arrives, and the platform posts a price before observing the traders' valuations.
arXiv:2608. 11560v1 Announce Type: new Abstract: Personalizing marketing messages with contextual multi-armed bandits (CMABs) drives real business value, yet the objective that ultimately matters - a downstream conversion - is observed only weeks later, too late to drive online learning.
arXiv:2608. 11419v1 Announce Type: new Abstract: Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants.