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Simple and Efficient Budget Feasible Mechanisms for Monotone Submodular Valuations

Published: 29 January 2021 Publication History

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 requires the design a mechanism that 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. We consider the general case where the buyer’s valuation is a monotone submodular function. There are a number of truthful mechanisms known for this problem. We offer two general frameworks for simple mechanisms, and by combining these frameworks, we significantly improve on the best known results, 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 2r2. We also provide a mechanism without the large market assumption, where we achieve a 4r + 1 approximation guarantee. We also show how our results can be used for the problem of a principal hiring in a Crowdsourcing Market to select a set of tasks subject to a total budget.

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    cover image ACM Transactions on Economics and Computation
    ACM Transactions on Economics and Computation  Volume 9, Issue 1
    Special Issue on WINE'18: Part 1, and Regular Papers
    March 2021
    182 pages
    ISSN:2167-8375
    EISSN:2167-8383
    DOI:10.1145/3446654
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 29 January 2021
    Accepted: 01 March 2020
    Revised: 01 November 2019
    Received: 01 April 2019
    Published in�TEAC�Volume 9, Issue 1

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    Author Tags

    1. Budget feasible mechanisms
    2. algorithmic game theory
    3. algorithmic mechanism design
    4. submodular valuations

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    View all
    • (2023)Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular ObjectivesProceedings of the ACM Web Conference 202310.1145/3543507.3583477(3530-3540)Online publication date: 30-Apr-2023
    • (2023)Freshness-Aware Incentive Mechanism for Mobile Crowdsensing With Budget ConstraintIEEE Transactions on Services Computing10.1109/TSC.2023.331617016:6(4248-4260)Online publication date: Nov-2023
    • (2023)Hiring a Team From Social Network: Incentive Mechanism Design for Two-Tiered Social Mobile CrowdsourcingIEEE Transactions on Mobile Computing10.1109/TMC.2022.316210822:8(4664-4681)Online publication date: 1-Aug-2023
    • (2023)Auction Design for�Value Maximizers with�Budget and�Return-on-Spend ConstraintsWeb and Internet Economics10.1007/978-3-031-48974-7_27(474-491)Online publication date: 31-Dec-2023

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