Single-shot auctions are commonly used as a means to sell goods, for example when selling ad space or allocating radio frequencies, however devising mechanisms for auctions with multiple bidders and multiple items can be complicated. It has been shown that neural networks can be used to approximate optimal mechanisms while satisfying the constraints that an auction be strategyproof and individually rational. We show that despite such auctions maximizing revenue, they do so at the cost of revealing private bidder information. While randomness is often used to build in privacy, in this context it comes with complications if done without care. Specifically, it can violate rationality and feasibility constraints, fundamentally change the incentive structure of the mechanism, and/or harm top-level metrics such as revenue and social welfare. We propose a method that employs stochasticity to improve privacy while meeting the requirements for auction mechanisms with only a modest sacrifice in revenue. We analyze the cost to the auction house that comes with introducing varying degrees of privacy in common auction settings. Our results show that despite current neural auctions' ability to approximate optimal mechanisms, the resulting vulnerability that comes with relying on neural networks must be accounted for.
翻译:单拍拍卖通常被用作出售货物的手段,例如出售广告空间或分配无线电频率时,但设计与多个投标人和多个项目拍卖的机制可能会变得复杂。已经表明,神经网络可以用来估计最佳机制,同时满足拍卖具有战略性和个别理性的限制;我们表明,尽管拍卖使收入最大化,但这样做的代价是披露私人出价人的信息。虽然随机性往往被用来在隐私中建立,在这种情况下,如果不加注意地这样做,就会发生并发症。具体地说,它可能违反合理性和可行性限制,从根本上改变机制的激励结构,并(或)损害收入和社会福利等最高尺度。我们建议一种方法,在满足拍卖机制要求的同时,利用随机性来改善隐私,同时只作出少量收入牺牲。我们分析拍卖所付出的代价,在普通拍卖环境中引入不同程度的隐私。我们的结果表明,尽管当前的神经拍卖能够估计最佳机制,但由此产生的依赖神经网络的脆弱性必须加以说明。</s>