GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
<|reference_start|>GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning: Differentiable economics uses deep learning for automated mechanism design. Despite strong progress, it has remained an open problem to learn multi-bidder, general, and fully strategy-proof (SP) auctions. We introduce GEneral Menu-based NETwork (GemNet), which significantly extends the menu-based approach of RochetNet [D\"utting et al., 2023] to the multi-bidder setting. The challenge in achieving SP is to learn bidder-independent menus that are feasible, so that the optimal menu choices for each bidder do not over-allocate items when taken together (we call this menu compatibility). GemNet penalizes the failure of menu compatibility during training, and transforms learned menus after training through price changes, by considering a set of discretized bidder values and reasoning about Lipschitz smoothness to guarantee menu compatibility on the entire value space. This approach is general, leaving undisturbed trained menus that already satisfy menu compatibility and reducing to RochetNet for a single bidder. Mixed-integer linear programs are used for menu transforms and through a number of optimizations, including adaptive grids and methods to skip menu elements, we scale to large auction design problems. GemNet learns auctions with better revenue than affine maximization methods, achieves exact SP whereas previous general multi-bidder methods are approximately SP, and offers greatly enhanced interpretability.<|reference_end|>
@article{wang2024gemnet:,
    title={GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep
  Learning},
    author={Tonghan Wang, Yanchen Jiang, David C. Parkes},
    journal={arXiv preprint arXiv:2406.07428},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07428},
    primaryClass={cs.GT cs.AI cs.LG}
}