Optimal Competitive Auctions
Ning Chen, Nick Gravin, Pinyan Lu

TL;DR
This paper characterizes and constructs optimal truthful auctions for digital goods, achieving the best worst-case revenue ratios for key benchmarks and confirming longstanding conjectures in auction theory.
Contribution
It provides a necessary and sufficient condition for competitive ratios, constructs optimal auctions for two benchmarks, and settles a major open problem in digital goods auction design.
Findings
Optimal auctions match known lower bounds for the benchmark ^{(2)}.
Confirmed the conjecture that existing bounds are tight.
Derived the optimal competitive ratio for limited-supply Vickrey auctions as (n/(n-1))^{n-1}-1.
Abstract
We study the design of truthful auctions for selling identical items in unlimited supply (e.g., digital goods) to n unit demand buyers. This classic problem stands out from profit-maximizing auction design literature as it requires no probabilistic assumptions on buyers' valuations and employs the framework of competitive analysis. Our objective is to optimize the worst-case performance of an auction, measured by the ratio between a given benchmark and revenue generated by the auction. We establish a sufficient and necessary condition that characterizes competitive ratios for all monotone benchmarks. The characterization identifies the worst-case distribution of instances and reveals intrinsic relations between competitive ratios and benchmarks in the competitive analysis. With the characterization at hand, we show optimal competitive auctions for two natural benchmarks. The most…
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Taxonomy
TopicsAuction Theory and Applications · Optimization and Search Problems · Consumer Market Behavior and Pricing
