PreferenceNet：用深度学习在拍卖设计中编码人类偏好
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
摘要：
最优拍卖的设计是经济学、博弈论和计算机科学中的一个重要问题。尽管经过几十年的努力，在受限环境之外，防策略的、收入最大化的拍卖设计仍然是未知的。然而，最近使用深度学习的方法在近似最优拍卖、恢复几个已知解决方案以及在最优拍卖未知时优于强基线方面取得了一些成功。除了收入最大化之外，拍卖机制还可能寻求鼓励社会所希望的约束，如分配公平性或多样性。然而，这些哲学概念既没有标准化，也没有被广泛接受的正式定义。在本文中，我们提出了PreferenceNet，现有的基于神经网络的拍卖机制的扩展编码的限制使用（潜在的人类提供的）样本的理想的分配。此外，我们引入了一个新的指标来评估拍卖分配的遵守这样的社会理想的约束，并证明我们提出的方法是有竞争力的，与目前国家的最先进的基于神经网络的拍卖设计。我们通过人类受试者研究验证了我们的方法，并表明我们能够有效地捕捉真实的人类偏好。我们的代码可在https://github.com/neeharperi/PreferenceNet上获得
英文摘要：
The design of optimal auctions is a problem of interest in economics, game theory and computer science. Despite decades of effort, strategyproof, revenue-maximizing auction designs are still not known outside of restricted settings. However, recent methods using deep learning have shown some success in approximating optimal auctions, recovering several known solutions and outperforming strong baselines when optimal auctions are not known. In addition to maximizing revenue, auction mechanisms may also seek to encourage socially desirable constraints such as allocation fairness or diversity. However, these philosophical notions neither have standardization nor do they have widely accepted formal definitions. In this paper, we propose PreferenceNet, an extension of existing neural-network-based auction mechanisms to encode constraints using (potentially human-provided) exemplars of desirable allocations. In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs. We validate our approach through human subject research and show that we are able to effectively capture real human preferences. Our code is available at https://github.com/neeharperi/PreferenceNet
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。
