When Will You Become the Best Reviewer of Your Own Papers? An Owner-Assisted Approach to Mechanism Design

Weijie Su (University of Pennsylvania)

Alice submits a number of papers to a machine learning conference and has knowledge of the quality of her papers. Given noisy grades rated by independent reviewers, can Bob obtain accurate estimates of the ground-truth quality of the papers by asking Alice a question about the ground truth? In this talk, we address this when the payoff of Alice is additive convex utility over all her papers. First, if Alice would truthfully answer the question because by doing so her payoff is maximized, we show that the questions must be formulated as pairwise comparisons between her papers. Moreover, if Alice is required to provide a ranking of her papers, which is the most fine-grained question via pairwise comparisons, we prove that she would be truth-telling. By incorporating the ground-truth ranking, we show that Bob can obtain an estimator with the optimal squared error in certain regimes based on any possible ways of truthful information elicitation. Moreover, the estimated grades are substantially more accurate than the raw grades when the number of papers is large and the raw grades are very noisy. Finally, we conclude the talk with several extensions and some refinements for practical considerations. This is based on a working paper (tinyurl.com/4f7pnfk6) and arXiv:2110.14802.


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