Finding the optimum design of a complex auction is a challenging and important economic problem. Multi-agent deep learning can help find equilibria by making use of inherent symmetries in bidding strategies.
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References
Ausubel, L. M. & Baranov, O. Int. J. Game Theory 49, 251–273 (2020).
Bichler, M., Fichtl, M., Heiderkrüger, S., Kohring, N. & Sutterer, P. Nat. Mach. Intell. https://doi.org/10.1038/s42256-021-00365-4 (2021).
Jordan, J. S. Games Econ. Behav. 5, 368–386 (1993).
Mazumdar, E., Ratliff, L. J. & Sastry, S. S. SIAM J. Math. Data Sci. 2, 103–131 (2020).
Dütting, P., Feng, Z., Narasimhan, H., Parkes, D. C. & Ravindranath, S. S. In Proc. 36th Int. Conf. Machine Learning Vol. 97, 1706–1715 (PMLR, 2019).
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Parkes, D.C. Playing with symmetry with neural networks. Nat Mach Intell 3, 658 (2021). https://doi.org/10.1038/s42256-021-00380-5
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DOI: https://doi.org/10.1038/s42256-021-00380-5
