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20242026
most citedOnline Optimization Algorithms in Repeated Price Competition: Equilibrium Learning and Algorithmic Collusion

1 citations · 1 across the 2 of their papers we have counts for

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11 papers

cs.GT20261 cited

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…

cs.GT2026

Convergence of Stochastic First-Order Algorithms in Bertrand Competition Under Incomplete Information

Martin Bichler, Jan-Sebastian Hoehener

Autonomous pricing agents are widely deployed in online marketplaces, making algorithmic pricing a prominent application of multi-agent learning. Experimental studies often report…

econ.TH2026

Distributionally Robust Contract Design with Deferred Inspection

Halil I. Bayrak, Martin Bichler

We study a robust contract design problem with deferred inspection, in which a principal allocates a scarce resource to an agent, observes the agent's realized outcome ex post at n…

cs.GT2025

Algorithmic Predation: Equilibrium Analysis in Dynamic Oligopolies with Smooth Market Sharing

Fabian Raoul Pieroth, Ole Petersen, Martin Bichler

Predatory pricing -- where a firm strategically lowers prices to undermine competitors -- is a contentious topic in dynamic oligopoly theory, with scholars debating practical relev…

cs.GT2025

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…

cs.GT2025

Semicoarse Correlated Equilibria and LP-Based Guarantees for Gradient Dynamics in Normal-Form Games

Mete Şeref Ahunbay, Martin Bichler

Projected gradient ascent is known to satisfy no-external regret as a learning algorithm. However, recent empirical work shows that projected gradient ascent often finds the Nash e…