Reputation-based incentive protocols in crowdsourcing applications

By Yu Zhang, Mihaela van der Schaar. 2012 Proceedings IEEE INFOCOM, 2011

301 citations recorded by the source corpus.

Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in order to improve the performance of the non-cooperative equilibria emerging in such applications. We first formulate the exchanges on a crowdsourcing website as a two-sided market where requesters and workers are matched and play gift-giving games repeatedly. Subsequently, we study the protocol designer's problem of finding an optimal and sustainable (equilibrium) protocol which achieves the highest social welfare for that website. We prove that the proposed incentives protocol can make the website operate close to Pareto efficiency. Moreover, we also examine an alternative scenario, where the protocol designer aims at maximizing the revenue of the website and evaluate the performance of the optimal protocol.

Subfields: Computer Science Applications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bf426577c520fad1d21ecc386355dc0a7a5ae3c0

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Citations in the corpus

Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones (2016, background): “Zhang and van der Schaar [49] proposed reputation-based incentive mechanisms for crowdsourcing, where participants will earn reputations upon the completion of a task.”
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2019, methodology): “Speciﬁcally, by combining the Multi-KRUM [51], [52] and the reputation-based incentive protocols [53], an incentive mechanism is designed to prevent the poisoning attack as well as reward contributors properly.”
TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing (2014, background): “Unfortunately, most of existing designs of incentive mechanism [11] [12] have neglected this important dimension of location information in their designs.”
Optimal incentive-driven design of participatory sensing systems (2013, background): “In [13], the authors consider a reputation-based scheme in the context of a crowd-sourcing website.”
CrowdBC: A Blockchain-Based Decentralized Framework for Crowdsourcing (2019, methodology): “In our framework, we build the reputation-based incentive mechanism based on [13], which will be described in Section 6.”