J. M. Meylahn PhD (Janusz)

den Boer, A. , Meylahn, J., & Schinkel, M. P. (2022). Artificial Collusion: Examining Supracompetitive Pricing by Q-Learning Algorithms. (pp. 1-49). (Amsterdam Law School Research Paper; No. 2022-25), (Amsterdam Center for Law & Economics Working Paper ; No. 2022-06). Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.4213600

Meylahn, J., & Barfuss, W. (2022). Intrinsic fluctuations of reinforcement learning promote cooperation. (pp. 1-9). ArXiv.org. https://doi.org/10.48550/arXiv.2209.01013

Meylahn, J. , & den Boer, A. (2022). Learning to Collude in a Pricing Duopoly. Manufacturing & service operations management, 24(5), 2577–2594. https://doi.org/10.1287/msom.2021.1074

Meylahn, J. M., & Janssen, L. (2022). Limiting Dynamics for Q-Learning with Memory One in Symmetric Two-Player, Two-Action Games. Complexity, 2022, [4830491]. https://doi.org/10.1155/2022/4830491