20,292 research outputs found
An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets
In a crowdsourcing market, a requester is looking to form a team of workers
to perform a complex task that requires a variety of skills. Candidate workers
advertise their certified skills and bid prices for their participation. We
design four incentive mechanisms for selecting workers to form a valid team
(that can complete the task) and determining each individual worker's payment.
We examine profitability, individual rationality, computational efficiency, and
truthfulness for each of the four mechanisms. Our analysis shows that TruTeam,
one of the four mechanisms, is superior to the others, particularly due to its
computational efficiency and truthfulness. Our extensive simulations confirm
the analysis and demonstrate that TruTeam is an efficient and truthful pricing
mechanism for team formation in crowdsourcing markets.Comment: 6 pages, 3 figures, IEEE ICC 201
Incentivizing the Workers for Truth Discovery in Crowdsourcing with Copiers
Crowdsourcing has become an efficient paradigm for performing large scale
tasks. Truth discovery and incentive mechanism are fundamentally important for
the crowdsourcing system. Many truth discovery methods and incentive mechanisms
for crowdsourcing have been proposed. However, most of them cannot be applied
to deal with the crowdsourcing with copiers. To address the issue, we formulate
the problem of maximizing the social welfare such that all tasks can be
completed with the least confidence for truth discovery. We design an incentive
mechanism consisting truth discovery stage and reverse auction stage. In truth
discovery stage, we estimate the truth for each task based on both the
dependence and accuracy of workers. In reverse auction stage, we design a
greedy algorithm to select the winners and determine the payment. Through both
rigorous theoretical analysis and extensive simulations, we demonstrate that
the proposed mechanisms achieve computational efficiency, individual
rationality, truthfulness, and guaranteed approximation. Moreover, our truth
discovery method shows prominent advantage in terms of precision when there are
copiers in the crowdsourcing systems.Comment: 12 pages, 8 figure
Bayesian Budget Feasibility with Posted Pricing
We consider the problem of budget feasible mechanism design proposed by
Singer (2010), but in a Bayesian setting. A principal has a public value for
hiring a subset of the agents and a budget, while the agents have private costs
for being hired. We consider both additive and submodular value functions of
the principal. We show that there are simple, practical, ex post budget
balanced posted pricing mechanisms that approximate the value obtained by the
Bayesian optimal mechanism that is budget balanced only in expectation. A main
motivating application for this work is the crowdsourcing large projects, e.g.,
on Mechanical Turk, where workers are drawn from a large population and posted
pricing is standard. Our analysis methods relate to contention resolution
schemes in submodular optimization of Vondrak et al. (2011) and the correlation
gap analysis of Yan (2011)
General Privacy-Preserving Verifiable Incentive Mechanism for Crowdsourcing Markets
In crowdsourcing markets, there are two different type jobs, i.e. homogeneous
jobs and heterogeneous jobs, which need to be allocated to workers. Incentive
mechanisms are essential to attract extensive user participating for achieving
good service quality, especially under a given budget constraint condition. To
this end, recently, Singer et al. propose a novel class of auction mechanisms
for determining near-optimal prices of tasks for crowdsourcing markets
constrained by the given budget. Their mechanisms are very useful to motivate
extensive user to truthfully participate in crowdsourcing markets. Although
they are so important, there still exist many security and privacy challenges
in real-life environments. In this paper, we present a general
privacy-preserving verifiable incentive mechanism for crowdsourcing markets
with the budget constraint, not only to exploit how to protect the bids and
assignments' privacy, and the chosen winners' privacy in crowdsourcing markets
with homogeneous jobs and heterogeneous jobs and identity privacy from users,
but also to make the verifiable payment between the platform and users for
crowdsourcing applications. Results show that our general privacy-preserving
verifiable incentive mechanisms achieve the same results as the generic one
without privacy preservation.Comment: This paper has been withdrawn by the author due to a crucial sign
error in equation 1 and Figure
Rating Protocol Design for Extortion and Cooperation in the Crowdsourcing Contest Dilemma
Crowdsourcing has emerged as a paradigm for leveraging human intelligence and
activity to solve a wide range of tasks. However, strategic workers will find
enticement in their self-interest to free-ride and attack in a crowdsourcing
contest dilemma game. Hence, incentive mechanisms are of great importance to
overcome the inefficiency of the socially undesirable equilibrium. Existing
incentive mechanisms are not effective in providing incentives for cooperation
in crowdsourcing competitions due to the following features: heterogeneous
workers compete against each other in a crowdsourcing platform with imperfect
monitoring. In this paper, we take these features into consideration, and
develop a novel game-theoretic design of rating protocols, which integrates
binary rating labels with differential pricing to maximize the requester's
utility, by extorting selfish workers and enforcing cooperation among them. By
quantifying necessary and sufficient conditions for the sustainable social
norm, we formulate the problem of maximizing the revenue of the requester among
all sustainable rating protocols, provide design guidelines for optimal rating
protocols, and design a low-complexity algorithm to select optimal design
parameters which are related to differential punishments and pricing schemes.
Simulation results demonstrate how intrinsic parameters impact on design
parameters, as well as the performance gain of the proposed rating protocol.Comment: 13 pages, 21 figure
Beyond AMT: An Analysis of Crowd Work Platforms
While Amazon's Mechanical Turk (AMT) helped launch the paid crowd work
industry eight years ago, many new vendors now offer a range of alternative
models. Despite this, little crowd work research has explored other platforms.
Such near-exclusive focus risks letting AMT's particular vagaries and
limitations overly shape our understanding of crowd work and the research
questions and directions being pursued. To address this, we present a
cross-platform content analysis of seven crowd work platforms. We begin by
reviewing how AMT assumptions and limitations have influenced prior research.
Next, we formulate key criteria for characterizing and differentiating crowd
work platforms. Our analysis of platforms contrasts them with AMT, informing
both methodology of use and directions for future research. Our cross-platform
analysis represents the only such study by researchers for researchers,
intended to further enrich the diversity of research on crowd work and
accelerate progress
Simple and Efficient Budget Feasible Mechanisms for Monotone Submodular Valuations
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 approximation algorithm for the optimization problem
(ignoring incentives), our mechanism is a approximation mechanism for
large markets, an improvement from . We also provide a similar
parameterized mechanism without the large market assumption, where we achieve a
approximation guarantee
Optimal Prizes for All-Pay Contests in Heterogeneous Crowdsourcing
Incentives are key to the success of crowdsourcing which heavily depends on
the level of user participation. This paper designs an incentive mechanism to
motivate a heterogeneous crowd of users to actively participate in
crowdsourcing campaigns. We cast the problem in a new, asymmetric all-pay
contest model with incomplete information, where an arbitrary n of users exert
irrevocable effort to compete for a prize tuple. The prize tuple is an array of
prize functions as opposed to a single constant prize typically used by
conventional contests. We design an optimal contest that (a) induces the
maximum profit---total user effort minus the prize payout---for the
crowdsourcer, and (b) ensures users to strictly have the incentive to
participate. In stark contrast to intuition and prior related work, our
mechanism induces an equilibrium in which heterogeneous users behave
independently of one another as if they were in a homogeneous setting. This
newly discovered property, which we coin as strategy autonomy (SA), is of
practical significance: it (a) reduces computational and storage complexity by
n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting
an effort reservation effect existing in asymmetric contests, and (c)
neutralizes the (almost universal) law of diminishing marginal returns (DMR).
Through an extensive numerical case study, we demonstrate and scrutinize the
superior profitability of our mechanism, as well as draw insights into the SA
property.Comment: 9 pages, 4 figures. IEEE MASS 201
On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing
While microtask crowdsourcing provides a new way to solve large volumes of
small tasks at a much lower price compared with traditional in-house solutions,
it suffers from quality problems due to the lack of incentives. On the other
hand, providing incentives for microtask crowdsourcing is challenging since
verifying the quality of submitted solutions is so expensive that will negate
the advantage of microtask crowdsourcing. We study cost-effective incentive
mechanisms for microtask crowdsourcing in this paper. In particular, we
consider a model with strategic workers, where the primary objective of a
worker is to maximize his own utility. Based on this model, we analyze two
basic mechanisms widely adopted in existing microtask crowdsourcing
applications and show that, to obtain high quality solutions from workers,
their costs are constrained by some lower bounds. We then propose a
cost-effective mechanism that employs quality-aware worker training as a tool
to stimulate workers to provide high quality solutions. We prove theoretically
that the proposed mechanism, when properly designed, can obtain high quality
solutions with an arbitrarily low cost. Beyond its theoretical guarantees, we
further demonstrate the effectiveness of our proposed mechanisms through a set
of behavioral experiments
A Budget Feasible Peer Graded Mechanism For IoT-Based Crowdsourcing
We develop and extend a line of recent works on the design of mechanisms for
heterogeneous tasks assignment problem in 'crowdsourcing'. The budgeted market
we consider consists of multiple task requesters and multiple IoT devices as
task executers; where each task requester is endowed with a single distinct
task along with the publicly known budget. Also, each IoT device has valuations
as the cost for executing the tasks and quality, which are private. Given such
scenario, the objective is to select a subset of IoT devices for each task,
such that the total payment made is within the allotted quota of the budget
while attaining a threshold quality. For the purpose of determining the unknown
quality of the IoT devices, we have utilized the concept of peer grading. In
this paper, we have carefully crafted a truthful budget feasible mechanism;
namely TUBE-TAP for the problem under investigation that also allows us to have
the true information about the quality of the IoT devices. The simulations are
performed in order to measure the efficacy of our proposed mechanism.Comment: In Version 2, errors are fixe
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