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.
By Yuhang Wu, Assaf Zeevi
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.
By Dantong Chu, Xuefeng Gao, Yufei Zhang
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.
By Jackie Baek, Vivek F. Farias, Farrell Wu
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.
By Maria-Florina Balcan, Tejas Pagare, Karan Singh
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?
By Shengyu Cao, Ming Hu
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.
By Panayotis Mertikopoulos
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
By Rui Ai, David Simchi-Levi, Feng Zhu
arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.
By Andreas Athanasopoulos, Christos Dimitrakakis
arXiv:2610.00619v1 Announce Type: cross
Abstract: In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that d...
By Christos Spyridon Koulouris, Carlo Campajola
The paper examines whether autonomous learning-based agents in electricity markets can develop tacit collusion without explicit coordination. By modeling strategic bidding as a repeated game with imperfect public monitoring and employing multi-agent reinforcement learning, the authors identify conditions under which agents achieve supra-competitive outcomes. Their experiments demonstrate that such collusive behavior can emerge naturally, highlighting a realistic risk for algorithmic electricity markets.
By Jakub Seredy\'nski, Georgios Tsaousoglou
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
By Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
arXiv:2410.14839v5 Announce Type: replace-cross
Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corpor...
By Adel Javanmard, Jingwei Ji, Renyuan Xu