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Results: 7
Number of items: 7
  • Open Access
    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. (2025). Collective cooperative intelligence. Proceedings of the National Academy of Sciences, 122(25), Article e2319948121. https://doi.org/10.1073/pnas.2319948121
  • Open Access
    Barfuss, W., & Meylahn, J. M. (2023). Intrinsic fluctuations of reinforcement learning promote cooperation. Scientific Reports, 13, Article 1309. https://doi.org/10.1038/s41598-023-27672-7
  • Open Access
    van Beurden, A. W., Meylahn, J. M., Achterhof, S., Buijink, R., Olde Engberink, A., Michel, S., Meijer, J. H., & Rohling, J. H. T. (2023). Reduced Plasticity in Coupling Strength in the Aging SCN Clock as Revealed by Kuramoto Modeling. Journal of biological rhythms, 38(5), 461-475. https://doi.org/10.1177/07487304231175191
  • Meylahn, J. M., & den Boer, A. V. (2022). Learning to Collude in a Pricing Duopoly. Manufacturing and Service Operations Management, 24(5), 2577-2594. https://doi.org/10.1287/msom.2021.1074
  • Open Access
    den Boer, A. V., Meylahn, J. M., & Schinkel, M. P. (2022). Artificial Collusion: Examining Supra-competitive Pricing by Autonomous Q-learning Algorithms. (Amsterdam Law School Legal Studies Research Paper; Vol. 2022-25), (Amsterdam Center for Law & Economics Working Paper; Vol. 2022-06). University of Amsterdam. https://doi.org/10.2139/ssrn.4213600
  • Open Access
    Achterhof, S., & Meylahn, J. M. (2021). Two-community noisy Kuramoto model with general interaction strengths. I. Chaos, 31(3), Article 033115. https://doi.org/10.1063/5.0022624
  • Open Access
    Achterhof, S., & Meylahn, J. M. (2021). Two-community noisy Kuramoto model with general interaction strengths. II. Chaos, 31(3), Article 033116. https://doi.org/10.1063/5.0022625
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