Randomized learning-augmented auctions with revenue guarantees
CoRR(2024)
摘要
We consider the fundamental problem of designing a truthful single-item
auction with the challenging objective of extracting a large fraction of the
highest agent valuation as revenue. Following a recent trend in algorithm
design, we assume that the agent valuations belong to a known interval, and a
(possibly erroneous) prediction for the highest valuation is available. Then,
auction design aims for high consistency and robustness, meaning that, for
appropriate pairs of values γ and ρ, the extracted revenue should
be at least a γ- or ρ-fraction of the highest valuation when the
prediction is correct for the input instance or not. We characterize all pairs
of parameters γ and ρ so that a randomized γ-consistent and
ρ-robust auction exists. Furthermore, for the setting in which robustness
can be a function of the prediction error, we give sufficient and necessary
conditions for the existence of robust auctions and present randomized auctions
that extract a revenue that is only a polylogarithmic (in terms of the
prediction error) factor away from the highest agent valuation.
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