dr. J. M. Meylahn (Janusz)

Universitair docent
EEMCS-AM-MOR
Zilverling 4009
j.m.meylahn@utwente.nl
+31534895431
Visitekaartje

Expertises

  • Social Sciences

    • Learning
    • Fluctuation
    • Reinforcement
    • Memory

  • Computer Science

    • Reinforcement Learning
    • Learning Algorithm
    • Synchronization

  • Mathematics

    • Interaction

Organisaties

Publicaties

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

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