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
    • Learning Algorithm
    • Synchronization

  • 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

2024
How social reinforcement learning can lead to metastable polarisation and the voter model. ArXiv.org. Meylahn, B. V. & Meylahn, J. M.https://doi.org/10.48550/arXiv.2406.07993
2023
Intrinsic fluctuations of reinforcement learning promote cooperation, 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, 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/10.1177/07487304231175191
2022
Artificial Collusion: Examining Supracompetitive Pricing by Q-Learning Algorithms, 1-49. 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, Article 4830491. Meylahn, J. M. & Janssen, L.https://doi.org/10.1155/2022/4830491Learning to Collude in a Pricing Duopoly, 2577–2594. Meylahn, J. & den Boer, A.https://doi.org/10.1287/msom.2021.1074Intrinsic fluctuations of reinforcement learning promote cooperation, 1-9. ArXiv.org. Meylahn, J. & Barfuss, W.https://doi.org/10.48550/arXiv.2209.01013
2021
Two-community noisy Kuramoto model with general interaction strengths. I, Article 033115. Achterhof, S. & Meylahn, J. M.https://doi.org/10.1063/5.0022624Two-community noisy Kuramoto model with general interaction strengths. II, Article 033116. Achterhof, S. & Meylahn, J. M.https://doi.org/10.1063/5.0022625
2020
Two-Community Noisy Kuramoto Model Suggests Mechanism for Splitting in the Suprachiasmatic Nucleus, 158-166. Rohling, J. H. T. & Meylahn, J. M.https://doi.org/10.1177/0748730419898314

Research profiles

I am responsible for teaching a number of course, including

  1. Social Network Structure and Dynamics (Bachelor)
  2. Complex Networks (Honours)
  3. Stochastic Processes (Masters)

Additionally, I supervise internships, bachelor projects and master projects. 

Courses academic year 2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2022/2023

Address

University of Twente

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

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Organisations

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