In mechanism design, it is challenging to design the optimal auction with correlated values in general settings. Although value distribution can be further exploited to improve revenue, the complex correlation structure makes it hard to acquire in practice. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we design a learning-based auction, which can encode the correlation of values into the rank score of each bidder, and further adjust the ranking rule to approach the optimal revenue. We strictly guarantee the property of strategy-proofness by encoding game theoretical conditions into the neural network structure. Furthermore, all operations in the designed auctions are differentiable to enable an end-to-end training paradigm. Experimental results demonstrate that the proposed auction mechanism can represent almost any strategy-proof auction mechanism, and outperforms the auction mechanisms wildly used in the correlated value settings.
翻译:在机制设计中,设计具有总体相关价值的最佳拍卖具有挑战性。尽管可以进一步利用价值分配来改善收入,但复杂的相关结构使得在实践中很难获得。数据驱动的拍卖机制,借助机器学习,能够直接从历史拍卖数据设计拍卖,而不必依赖特定价值分配。在这项工作中,我们设计了一个基于学习的拍卖,可以将价值的相互关系与每个出价人的分数相融合,并进一步调整排名规则以接近最佳收入。我们通过将游戏理论条件编码到神经网络结构,严格保证战略的可靠性。此外,设计拍卖中的所有操作都不同,能够形成一个端到端培训模式。实验结果表明,拟议的拍卖机制几乎可以代表任何战略性拍卖机制,并超越了相关价值环境中疯狂使用的拍卖机制。