Budget-Feasible Mechanism Design for Non-Monotone Submodular Objectives: Offline and Online

TL;DR

This paper introduces the first polynomial-time, truthful, budget-feasible mechanisms with constant approximation guarantees for non-monotone submodular objectives in both offline and online settings, advancing procurement auction design.

Contribution

It develops a novel greedy algorithm and mechanism for non-monotone submodular maximization under budget constraints, applicable in offline, online, and constrained settings.

Findings

First polynomial-time, truthful, budget-feasible mechanisms with O(1) approximation for non-monotone submodular functions

Mechanisms extend to online auctions and p-system constraints with competitive guarantees

Lower bounds indicate the near-optimality of the proposed mechanisms

Abstract

The framework of budget-feasible mechanism design studies procurement auctions where the auctioneer (buyer) aims to maximize his valuation function subject to a hard budget constraint. We study the problem of designing truthful mechanisms that have good approximation guarantees and never pay the participating agents (sellers) more than the budget. We focus on the case of general (non-monotone) submodular valuation functions and derive the first truthful, budget-feasible and $O(1)$-approximate mechanisms that run in polynomial time in the value query model, for both offline and online auctions. Prior to our work, the only $O(1)$-approximation mechanism known for non-monotone submodular objectives required an exponential number of value queries. At the heart of our approach lies a novel greedy algorithm for non-monotone submodular maximization under a knapsack constraint. Our algorithm builds two candidate solutions simultaneously (to achieve a good approximation), yet ensures that agents cannot jump from one solution to the other (to implicitly enforce truthfulness). Ours is the first mechanism for the problem where---crucially---the agents are not ordered with respect to their marginal value per cost. This allows us to appropriately adapt these ideas to the online setting as well. To further illustrate the applicability of our approach, we also consider the case where additional feasibility constraints are present. We obtain $O(p)$-approximation mechanisms for both monotone and non-monotone submodular objectives, when the feasible solutions are independent sets of a $p$-system. With the exception of additive valuation functions, no mechanisms were known for this setting prior to our work. Finally, we provide lower bounds suggesting that, when one cares about non-trivial approximation guarantees in polynomial time, our results are asymptotically best possible.