The growing demand for data and AI-generated digital goods, such as personalized written content and artwork, necessitates effective pricing and feedback mechanisms that account for uncertain utility and costly production. Motivated by these developments, this study presents a novel mechanism design addressing a general repeated-auction setting where the utility derived from a sold good is revealed post-sale. The mechanism's novelty lies in using pairwise comparisons for eliciting information from the bidder, arguably easier for humans than assigning a numerical value. Our mechanism chooses allocations using an epsilon-greedy strategy and relies on pairwise comparisons between realized utility from allocated goods and an arbitrary value, avoiding the learning-to-bid problem explored in previous work. We prove this mechanism to be asymptotically truthful, individually rational, and welfare and revenue maximizing. The mechanism's relevance is broad, applying to any setting with made-to-order goods of variable quality. Experimental results on multi-label toxicity annotation data, an example of negative utilities, highlight how our proposed mechanism could enhance social welfare in data auctions. Overall, our focus on human factors contributes to the development of more human-aware and efficient mechanism design.

翻译：随着对数据和AI生成的数字商品（如个性化文字内容和艺术品）的需求日益增长，需要有效的定价和反馈机制来应对不确定的效用和昂贵的生产成本。受这些发展的启发，本研究提出了一种新颖的机制设计，针对一种通用的重复拍卖场景，其中出售商品所产生的效用会在交易后揭示。该机制的创新之处在于利用成对比较来获取竞拍者的信息，这相比赋予数值而言，对人类而言更易实现。我们的机制采用ε-贪心策略选择分配，并依赖于已分配商品的实际效用与任意值之间的成对比较，从而避免了先前工作中探讨的学习出价问题。我们证明了该机制是渐进诚实的、个体理性的，并且能够最大化社会福利与收入。该机制的适用范围广泛，可应用于任何具有可变质量的定制商品场景。针对多标签毒性标注数据（一个负效用的例子）的实验结果，突出了我们提出的机制如何能够增强数据拍卖中的社会福利。总体而言，我们对人类因素的关注有助于开发更具人类意识和效率的机制设计。