Reputation-based incentive protocols in crowdsourcing applications
Open accessAbstract
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.
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The incentive mechanism prevents poisoning attacks.
“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.”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [53]
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Incentivize Multi-Class Crowd Labeling Under Budget Constraint
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A crowdsourcing logistics solution based on digital twin and four-party evolutionary game
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Adaptive contract design for crowdsourcing markets
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- Authors
- Zhang, Y., & van der Schaar, M.
- Affiliations
- University of California, Los Angeles (US)
- ORCID
- Zhang, Yu
- Venue
- 2012 Proceedings IEEE INFOCOM
- Publisher
- IEEE
- Published
- 2012-03
- Pages
- 2140-2148
- OpenAlex
- W2004373800
- Cited by
- 315
- References
- 16
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 16 · 2023: 8 · 2022: 15 · 2021: 20 · 2020: 31 · 2019: 50 · 2018: 27 · 2017: 32 · 2016: 27 · 2015: 38 · 2014: 23 · 2013: 15 · 2012: 5
- FWCI
- 17.87
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Conference on Computer Communications (INFOCOM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Auction Theory and Applications
- Keywords
- crowdsourcing, reputation mechanism, social norms, repeated games, two-sided markets, social welfare (welfare economics), Pareto optimality, protocol design, revenue maximization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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