No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctions
This addresses the need for efficient pricing in digital goods and data auctions, particularly for made-to-order items, though it is incremental in improving human-aware mechanism design.
The study tackled the problem of pricing digital goods and data with uncertain utility by introducing a mechanism that uses pairwise comparisons for feedback, proving it to be asymptotically truthful and welfare-maximizing, with experimental results showing potential welfare enhancements in data auctions.
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.