Problem
Research questions and friction points this paper is trying to address.
Auction Mechanism Design
Revenue Maximization
Asymmetric Valuation Distributions
Robust Auction Design with Support Information
🤖 AI Summary
This paper studies the robust auction design problem for a single item sold to multiple buyers with known support $[a,b]$ but unknown value distributions, under dominant-strategy incentive compatibility (DSIC). The objective is to minimize worst-case regret or approximation ratio. We establish the first unified robustness analysis framework that depends solely on the support—deriving tight asymptotic upper and lower bounds on both regret and approximation ratio. Crucially, we prove that the two-point distribution constitutes the worst-case instance. Furthermore, we construct an explicit class of piecewise-constant DSIC mechanisms that achieve these tight bounds. Our core contribution lies in characterizing the fundamental performance limitations imposed by support information alone, and—through minimax optimization and probabilistic uncertainty modeling—attaining simultaneous optimality in both theoretical bounds and mechanism construction.
📝 Abstract
A seller wants to sell an indivisible item to n buyers. The buyer valuations are drawn i.i.d. from a distribution, but the seller does not know this distribution; the seller only knows the support [a, b]. To be robust against the lack of knowledge of the environment and buyers' behavior, the seller optimizes over dominant strategy incentive compatible (DSIC) mechanisms, and measures the worst-case performance relative to an oracle with complete knowledge of buyers' valuations. Our analysis encompasses both the regret and the approximation ratio objectives.
Innovation
Methods, ideas, or system contributions that make the work stand out.
DSIC Mechanism
Robust Auction Design
POOL Auction Strategy
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