arXiv:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2608. 08268v1 Announce Type: cross Abstract: As firms increasingly deploy machine learning for strategic decision-making, understanding algorithmic interactions has become central to operations research and economics.
arXiv:2605. 16064v2 Announce Type: replace-cross Abstract: We study whether simple algorithmic pricing systems can systematically produce collusive-like prices in multi-firm markets.
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:2601. 01279v3 Announce Type: replace-cross Abstract: When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller feedback raise a key question: can standard AI deployment practices inadvertently produce supracompetitive pricing?
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.