1 citations · 1 across the 4 of their papers we have counts for
5 papers
Simultaneous Gradient Learning in First-Price Auctions
Janik Bürgermeister, Julius Durmann, Martin Bichler +3
We study equilibrium learning in discretized Bayesian games, focusing on first-price auctions as a testbed for gradient-based learning dynamics under private information. While the…
Mean-based algorithms: A lower bound and regret
Julius Durmann, Amelie Kleber
Mean-based algorithms are online learning algorithms that assign low probability to actions with low average rewards. Recent research shows that they converge to serially undominat…
Agentic Markets: Game Dynamics and Equilibrium in Markets with Learning Agents
Martin Bichler, Julius Durmann, Matthias Oberlechner
Autonomous and learning agents increasingly participate in markets - setting prices, placing bids, ordering inventory. Such agents are not just aiming to optimize in an uncertain e…
Algorithmic Pricing and Algorithmic Collusion
Martin Bichler, Julius Durmann, Matthias Oberlechner
The rise of algorithmic pricing in online retail platforms has attracted significant interest in how autonomous software agents interact under competition. This article explores th…
Online Optimization Algorithms in Repeated Price Competition: Equilibrium Learning and Algorithmic Collusion
Martin Bichler, Julius Durmann, Matthias Oberlechner
This paper investigates whether online learning algorithms in pricing produce competitive outcomes or tacit collusion. This issue has drawn considerable attention from competition…