A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders

TL;DR

This paper develops truthful mechanisms for combinatorial auctions with submodular bidders that approximate liquid welfare effectively, extending known methods and introducing market assumptions for improved performance.

Contribution

It adapts existing truthful mechanisms to optimize liquid welfare in budget-constrained combinatorial auctions, including new results for large markets and Bayesian settings.

Findings

Achieves an $O(\log m)$-approximate mechanism for liquid welfare.

Introduces the concept of competitive markets for constant-factor approximation.

Provides a truthful $O(1)$-approximate mechanism in Bayesian settings.

Abstract

We study incentive compatible mechanisms for Combinatorial Auctions where the bidders have submodular (or XOS) valuations and are budget-constrained. Our objective is to maximize the \emph{liquid welfare}, a notion of efficiency for budget-constrained bidders introduced by Dobzinski and Paes Leme (2014). We show that some of the known truthful mechanisms that best-approximate the social welfare for Combinatorial Auctions with submodular bidders through demand query oracles can be adapted, so that they retain truthfulness and achieve asymptotically the same approximation guarantees for the liquid welfare. More specifically, for the problem of optimizing the liquid welfare in Combinatorial Auctions with submodular bidders, we obtain a universally truthful randomized $O(\log m)$-approximate mechanism, where $m$ is the number of items, by adapting the mechanism of Krysta and V\"ocking (2012). Additionally, motivated by large market assumptions often used in mechanism design, we introduce a notion of competitive markets and show that in such markets, liquid welfare can be approximated within a constant factor by a randomized universally truthful mechanism. Finally, in the Bayesian setting, we obtain a truthful $O(1)$-approximate mechanism for the case where bidder valuations are generated as independent samples from a known distribution, by adapting the results of Feldman, Gravin and Lucier (2014).