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

Quantifying the likelihood of learning collusive strategy equilibria (2025)Chaos, 35(8). Article 083106. Meylahn, J. M.https://doi.org/10.1063/5.0281443Collective cooperative intelligence (2025)Proceedings of the National Academy of Sciences of the United States of America, 122(25). Article e2319948121. Barfuss, W., Flack, J., Gokhale, C. S., Hammond, L., Hilbe, C., Hughes, E., Leibo, J. Z., Lenaerts, T., Leonard, N., Levin, S., Sehwag, U. M., McAvoy, A., Meylahn, J. M. & Santos, F. P.https://doi.org/10.1073/pnas.2319948121Risk 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.4213600

Research profiles

Address

University of Twente

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

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