No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods
  and Data Auctions
Zachary Robertson Oluwasanmi Koyejo
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.