April 16, 2024, 4:45 a.m. | Sihan Zeng, Sujay Bhatt, Eleonora Kreacic, Parisa Hassanzadeh, Alec Koppel, Sumitra Ganesh

cs.LG updates on arXiv.org arxiv.org

arXiv:2311.10927v2 Announce Type: replace-cross
Abstract: We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for revenue maximization in auctions, we consider welfare maximization as the key objective in the payment-free setting. Without payment exchange, it is unclear how we can align agents' incentives to achieve the desired objectives of truthfulness and social welfare simultaneously, without resorting to approximations. Our work makes novel contributions by designing …

abstract agents arxiv cs.gt cs.lg design free key machine machine learning networks neural networks payment resources revenue the key type welfare

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