Simplicity in Auctions Revisited: The Primitive Complexity

TL;DR

This paper redefines simplicity in auction mechanisms through primitive complexity, analyzing bundle-size pricing and providing algorithms for revenue approximation with low complexity in complex valuation settings.

Contribution

It introduces a new measure of simplicity called primitive complexity and analyzes it for bundle-size pricing in complex valuation environments.

Findings

Low primitive complexity menus can approximate optimal revenue.

A randomized algorithm finds valuable sets with poly-logarithmic queries.

Finding most valuable sets of a given size is computationally hard for submodular valuations.

Abstract

In this paper we revisit the notion of simplicity in mechanisms. We consider a seller of $m$ items, facing a single buyer with valuation $v$. We observe that previous attempts to define complexity measures often fail to classify mechanisms that are intuitively considered simple (e.g., the "selling separately" mechanism) as such. We suggest to view a menu as simple if a bundle that maximizes the buyer's profit can be found by conducting a few primitive operations that are considered simple. The \emph{primitive complexity of a menu} is the number of primitive operations needed to (adaptively) find a profit-maximizing entry in the menu. In this paper, the primitive operation that we study is essentially computing the outcome of the "selling separately" mechanism. Does the primitive complexity capture the simplicity of other auctions that are intuitively simple? We consider \emph{bundle-size pricing}, a common pricing method in which the price of a bundle depends only on its size. Our main technical contribution is determining the primitive complexity of bundle-size pricing menus in various settings. We show that for any distribution $\cal D$ over weighted matroid rank valuations, even distributions with arbitrary correlation among their values, there is always a bundle-size pricing menu with low primitive complexity that achieves almost the same revenue as the optimal bundle-size pricing menu. As part of this proof we provide a randomized algorithm that for any weighted matroid rank valuation $v$ and integer $k$, finds the most valuable set of size $k$ with only a poly-logarithmic number of demand and value queries. We show that this result is essentially tight in several aspects. For example, if the valuation $v$ is submodular, then finding the most valuable set of size $k$ requires exponentially many queries (this solves an open question of Badanidiyuru et al. [EC'12]).