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

TL;DR

This paper introduces a novel reduction technique for multi-item, multi-bidder auctions that simplifies analysis and design of revenue-maximizing mechanisms, providing new bounds and mechanisms applicable to various distribution settings.

Contribution

The paper proposes the Best-Guess reduction, a new method to convert multi-bidder auctions into single-bidder auctions, enabling simpler analysis and new revenue guarantees.

Findings

Deterministic Best-Guess mechanism achieves a constant fraction of optimal revenue.

DSIC revenue is at least a constant fraction of BIC revenue under independence.

Closed-form expression for optimal revenue with identical distributions, achievable by 2nd-Price Bundling.

Abstract

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 $\Theta(k(r+\int_0^{mr} (1-F(x)^n) \ud x))$ 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.