121 citations · 244 across the 13 of their papers we have counts for
3 papers · 1 filter
Auditing Algorithmic Collusion from Strategy Graphs
Nicolas Eschenbaum, Janusz M. Meylahn
Detecting algorithmic collusion is challenging because regulators often have limited access to firms' algorithms, training data, and market information. We study an intermediate-in…
Equilibrium stability as a driver of cooperation among Q-learners
Janusz M. Meylahn, Maximilian Schäfer
Algorithmic collusion among pricing algorithms has raised concerns about sustained supra-competitive prices and their implications for social welfare. Existing work has largely foc…
Beyond the Independence Assumption: Finite-Sample Guarantees for Deep Q-Learning under -Mixing
Leon Halgryn, Sophie Langer, Janusz M. Meylahn +1
Finite-sample analyses of deep Q-learning typically treat replayed data as independent, even though it is sampled from temporally dependent state-action trajectories. We study the…