GemNet：基于菜单的、策略证明的多投标者拍卖通过深度学习
GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
AI总结：
GemNet通过深度学习扩展菜单方法至多投标者拍卖，确保策略证明性，提高收入与可解释性。
摘要：
自动机制设计（AMD）使用计算方法进行机制设计。可微经济学是AMD的一种形式，利用深度学习来学习机制设计，近年来在AMD领域取得了显著进展。然而，一个主要的开放问题是如何学习多投标者、通用且完全策略证明（SP）的拍卖。我们引入了通用基于菜单的网络（GemNet），它将单投标者RochetNet（Dütting等人，2024）的基于菜单的方法显著扩展到多投标者设置。实现SP的挑战在于学习可行且与投标者无关的菜单，使得每个投标者的最优菜单选择在组合时不会过度分配物品（我们称之为菜单兼容性）。GemNet在训练期间对菜单兼容性的失败进行惩罚，并在训练后通过价格变化转换学习到的菜单，通过考虑一组离散化的投标者价值并推理Lipschitz平滑性来保证整个价值空间上的菜单兼容性。这种方法具有通用性，保持已经满足菜单兼容性的训练菜单不变，并在单投标者情况下简化为RochetNet。使用混合整数线性规划进行菜单转换，并通过深度学习启发的多种优化，包括自适应网格和跳过菜单元素的方法，我们扩展到大型拍卖设计问题。GemNet学习的拍卖比仿射最大化方法获得更高的收入，实现了精确的SP，而之前的通用多投标者方法仅是近似SP，并且提供了大大增强的可解释性。
英文摘要：
Automated mechanism design (AMD) uses computational methods for mechanism design. Differentiable economics is a form of AMD that uses deep learning to learn mechanism designs and has enabled strong progress in AMD in recent years. Nevertheless, a major open problem has been 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 the single-bidder RochetNet (Dütting et al., 2024) 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 trained menus that already satisfy menu compatibility undisturbed and reducing to RochetNet for a single bidder. Mixed-integer linear programs are used for menu transforms, and through a number of optimizations enabled by deep learning, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
