Simple and Efficient Budget Feasible Mechanisms for Monotone Submodular Valuations

TL;DR

This paper introduces simple, efficient mechanisms for budget-constrained buyers with monotone submodular valuations, improving approximation guarantees and simplifying analysis in the design of truthful, budget-feasible mechanisms.

Contribution

The authors develop two frameworks for mechanisms that improve approximation ratios for monotone submodular valuations and simplify the mechanism design process.

Findings

Improved approximation guarantee from 7.91 to 5 for general case.

Enhanced guarantee from 3 to 2.58 in large markets.

Mechanism achieves a $r+1$ approximation given an $r$ approximation algorithm.

Abstract

We study the problem of a budget limited buyer who wants to buy a set of items, each from a different seller, to maximize her value. The budget feasible mechanism design problem aims to design a mechanism which incentivizes the sellers to truthfully report their cost, and maximizes the buyer's value while guaranteeing that the total payment does not exceed her budget. Such budget feasible mechanisms can model a buyer in a crowdsourcing market interested in recruiting a set of workers (sellers) to accomplish a task for her. This budget feasible mechanism design problem was introduced by Singer in 2010. There have been a number of improvements on the approximation guarantee of such mechanisms since then. We consider the general case where the buyer's valuation is a monotone submodular function. We offer two general frameworks for simple mechanisms, and by combining these frameworks, we significantly improve on the best known results for this problem, while also simplifying the analysis. For example, we improve the approximation guarantee for the general monotone submodular case from 7.91 to 5; and for the case of large markets (where each individual item has negligible value) from 3 to 2.58. More generally, given an $r$ approximation algorithm for the optimization problem (ignoring incentives), our mechanism is a $r+1$ approximation mechanism for large markets, an improvement from $2r^2$. We also provide a similar parameterized mechanism without the large market assumption, where we achieve a $4r+1$ approximation guarantee.