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.
The paper tackles the data market design problem, which seeks signaling schemes that maximize revenue for an information seller. It applies deep learning to learn these schemes, addressing both obedience and incentive constraints, and demonstrates that the framework can replicate known theoretical solutions, extend to more complex scenarios, and suggest new optimal designs. The study builds on prior auction‑design work and introduces a novel approach for revenue‑optimal data markets.
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:2506.03911v2 Announce Type: replace Abstract: Points-based rewards programs are a prevalent way to incentivize customer loyalty. In theory, the effective design of these programs is shaped by t...
arXiv:2606. 06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail.
The paper investigates whether pricing algorithms on multi‑seller platforms should incorporate competitors’ prices when learning demand. It compares two strategies: informed sellers that use competitor prices in their learning models, and oblivious sellers that ignore them. The study finds that oblivious sellers must explore prices more aggressively to offset missing competitor information; when all sellers are oblivious, prices eventually converge to the competitive outcome, but insufficient exploration can create many pseudo‑equilibria. In mixed markets, informed sellers earn more, and the unique Nash equilibrium is a fully informed market where prices efficiently converge to the competitive outcome, showing that oblivious modeling does not reliably produce collusion.
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
The paper introduces a robust framework for assortment optimization that addresses distributional shifts in customer choice behavior. It demonstrates computational tractability when the nominal choice model is known and develops statistically optimal algorithms for the data‑driven setting, providing matching upper and lower bounds on sample complexity. The authors identify "robust item‑wise coverage" as the minimal data requirement for efficient robust learning, bridging robustness and statistical efficiency in assortment planning.
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).