Zilverling

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 (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 promotes cooperation (2023)[Working paper › Preprint]. ArXiv.org. Armas, J., Merbis, W., Meylahn, J., Rad, S. R. & Razo, M. J. d.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.2021.1074Intrinsic fluctuations of reinforcement learning promote cooperation (2022)[Working paper › Working paper]. ArXiv.org. Meylahn, J. & Barfuss, W.https://doi.org/10.48550/arXiv.2209.01013

2021

Limiting dynamics for Q-learning with memory one in symmetric two-player, two-action games (2021)[Working paper › Preprint]. ArXiv.org. Meylahn, J. M. & Janssen, L.https://doi.org/10.48550/arXiv.2107.13995Two-community noisy Kuramoto model with general interaction strengths. I (2021)Chaos, 31(3). Article 033115. Achterhof, S. & Meylahn, J. M.https://doi.org/10.1063/5.0022624

Onderzoeksprofielen

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