Zilverling

dr. J. M. Meylahn (Janusz)

Assistant Professor
EEMCS-AM-MOR
Zilverling 4009
j.m.meylahn@utwente.nl
+31534895431
Business card

I'm an assistant professor in the Stochastic Operations Research group of the Applied Mathematics Department, working on multiagent learning. I studied Theoretical Physics at Stellenbosch University in South Africa and completed my PhD in Mathematics at Leiden University in 2019. 

Expertise

  • Social Sciences

    • Learning
    • Fluctuation
    • Reinforcement
    • Memory

  • Computer Science

    • Reinforcement Learning
    • Social Dilemma
    • Learning Algorithm

  • Mathematics

    • Interaction

Organisations

I currently work on (decentralized) multiagent reinforcement learning, with a focus on designing provably convergent algorithms with equilibrium selection control. My main area of application, is in the realm of algorithmic pricing. In this setting, I am interested in understanding if and how algorithms can learn to collude. 

Publications

2025

Risk aversion can promote cooperation (2025)Journal of Physics: Complexity, 6(1). Article 015010. Armas, J., Merbis, W., Meylahn, J. M., Rad, S. R. & Razo, M. J. d.https://doi.org/10.1088/2632-072X/adb234

2024

How social reinforcement learning can lead to metastable polarisation and the voter model (2024)PLoS ONE, 19(12). Article e0313951. Meylahn, B. V. & Meylahn, J. M.https://doi.org/10.1371/journal.pone.0313951Can Decentralized Q-learning learn to collude? (2024)[Contribution to conference › Paper] 17th European Workshop on Reinforcement Learning, EWRL 2024. Meylahn, J.How social reinforcement learning can lead to metastable polarisation and the voter model (2024)[Working paper › Preprint]. ArXiv.org. Meylahn, B. V. & Meylahn, J. M.https://doi.org/10.48550/arXiv.2406.07993

2023

Intrinsic fluctuations of reinforcement learning promote cooperation (2023)Scientific reports, 13(1). Article 1309. Barfuss, W. & Meylahn, J. M.https://doi.org/10.1038/s41598-023-27672-7Reduced Plasticity in Coupling Strength in the Aging SCN Clock as Revealed by Kuramoto Modeling (2023)Journal of Biological Rhythms, 38(5), 461-475. van Beurden, A. W., Meylahn, J. M., Achterhof, S., Buijink, R., Olde Engberink, A., Michel, S., Meijer, J. H. & Rohling, J. H. T.https://doi.org/10.1177/07487304231175191Risk aversion can promote cooperation (2023)[Working paper › Preprint]. ArXiv.org. Armas, J., Merbis, W., Meylahn, J., Rad, S. R. & del Razo, M. J.https://doi.org/10.48550/arXiv.2306.05971

2022

Artificial Collusion: Examining Supracompetitive Pricing by Q-Learning Algorithms (2022)[Working paper › Working paper]. Social Science Research Network (SSRN). den Boer, A. V., Meylahn, J. M. & Schinkel, M. P.https://doi.org/10.2139/ssrn.4213600Limiting Dynamics for Q-Learning with Memory One in Symmetric Two-Player, Two-Action Games (2022)Complexity, 2022. Article 4830491. Meylahn, J. M. & Janssen, L.https://doi.org/10.1155/2022/4830491Learning to Collude in a Pricing Duopoly (2022)Manufacturing & service operations management, 24(5), 2577–2594. Meylahn, J. & den Boer, A.https://doi.org/10.1287/msom.2021.1074

Research profiles

Address

University of Twente

Zilverling (building no. 11), room 4009
Hallenweg 19
7522 NH Enschede
Netherlands

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