An n-to-1 Bidder Reduction for Multi-item Auctions and its Applications

In this paper, we introduce a novel approach for reducing the $k$-item $n$-bidder auction with additive valuation to $k$-item $1$-bidder auctions. This approach, called the \emph{Best-Guess} reduction, can be applied to address several central questions in optimal revenue auction theory such as the power of randomization, and Bayesian versus dominant-strategy implementations. First, when the items have independent valuation distributions, we present a deterministic mechanism called {\it Deterministic Best-Guess} that yields at least a constant fraction of the optimal revenue by any randomized mechanism. Second, if all the $nk$ valuation random variables are independent, the optimal revenue achievable in {\it dominant strategy incentive compatibility} (DSIC) is shown to be at least a constant fraction of that achievable in {\it Bayesian incentive compatibility} (BIC). Third, when all the $nk$ values are identically distributed according to a common one-dimensional distribution $F$, the optimal revenue is shown to be expressible in the closed form $Θ(k(r+\int_0^{mr} (1-F(x)^n) \ud x))$ where $r= sup_{x\geq 0} \, x(1 - F(x)^n)$ and $m=\lceil k/n\rceil$; this revenue is achievable by a simple mechanism called \emph{2nd-Price Bundling}. All our results apply to arbitrary distributions, regular or irregular.

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