Simple Mechanisms For Agents With Complements

TL;DR

This paper analyzes the efficiency of simple auction mechanisms in the presence of complements, introducing a valuation hierarchy and providing bounds on the price of anarchy that degrade gracefully with the degree of complementarity.

Contribution

It introduces a valuation hierarchy that captures complementarity levels and provides new bounds on the price of anarchy for simple auctions within this hierarchy.

Findings

Price of anarchy is $O(d^2 \, \log(m/d))$ for valuations of level $d$.

Improved bound of $O(d \, \log m)$ for hypergraph representations with edges of size at most 2.

Randomized auction combination achieves a price of anarchy of at most $O(\sqrt{m})$.

Abstract

We study the efficiency of simple auctions in the presence of complements. [DMSW15] introduced the single-bid auction, and showed that it has a price of anarchy (PoA) of $O(\log m)$ for complement-free (i.e., subadditive) valuations. Prior to our work, no non-trivial upper bound on the PoA of single bid auctions was known for valuations exhibiting complements. We introduce a hierarchy over valuations, where levels of the hierarchy correspond to the degree of complementarity, and the PoA of the single bid auction degrades gracefully with the level of the hierarchy. This hierarchy is a refinement of the Maximum over Positive Hypergraphs (MPH) hierarchy [FFIILS15], where the degree of complementarity $d$ is captured by the maximum number of neighbors of a node in the positive hypergraph representation. We show that the price of anarchy of the single bid auction for valuations of level $d$ of the hierarchy is $O(d^2 \log(m/d))$, where $m$ is the number of items. We also establish an improved upper bound of $O(d \log m)$ for a subclass where every hyperedge in the positive hypergraph representation is of size at most 2 (but the degree is still $d$). Finally, we show that randomizing between the single bid auction and the grand bundle auction has a price of anarchy of at most $O(\sqrt{m})$ for general valuations. All of our results are derived via the smoothness framework, thus extend to coarse-correlated equilibria and to Bayes Nash equilibria.