Mitigating Herding in Hierarchical Crowdsourcing Networks

Abstract Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other...

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Autores principales:	Han Yu, Chunyan Miao, Cyril Leung, Yiqiang Chen, Simon Fauvel, Victor R. Lesser, Qiang Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3a2f8306e4384051a670ddd7ab355e30
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spelling	oai:doaj.org-article:3a2f8306e4384051a670ddd7ab355e302021-12-02T12:32:08ZMitigating Herding in Hierarchical Crowdsourcing Networks10.1038/s41598-016-0011-62045-2322https://doaj.org/article/3a2f8306e4384051a670ddd7ab355e302016-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-016-0011-6https://doaj.org/toc/2045-2322Abstract Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers’ current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.Han YuChunyan MiaoCyril LeungYiqiang ChenSimon FauvelVictor R. LesserQiang YangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 6, Iss 1, Pp 1-10 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Han Yu Chunyan Miao Cyril Leung Yiqiang Chen Simon Fauvel Victor R. Lesser Qiang Yang Mitigating Herding in Hierarchical Crowdsourcing Networks
description	Abstract Hierarchical crowdsourcing networks (HCNs) provide a useful mechanism for social mobilization. However, spontaneous evolution of the complex resource allocation dynamics can lead to undesirable herding behaviours in which a small group of reputable workers are overloaded while leaving other workers idle. Existing herding control mechanisms designed for typical crowdsourcing systems are not effective in HCNs. In order to bridge this gap, we investigate the herding dynamics in HCNs and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Sub-delegation approach with dynamic worker effort Pricing (RTS-P) - with objective functions aiming to achieve superlinear time-averaged collective productivity in an HCN. By considering the workers’ current reputation, workload, eagerness to work, and trust relationships, RTS-P provides a systematic approach to mitigate herding by helping workers make joint decisions on task sub-delegation, task acceptance, and effort pricing in a distributed manner. It is an individual-level decision support approach which results in the emergence of productive and robust collective patterns in HCNs. High resolution simulations demonstrate that RTS-P mitigates herding more effectively than state-of-the-art approaches.
format	article
author	Han Yu Chunyan Miao Cyril Leung Yiqiang Chen Simon Fauvel Victor R. Lesser Qiang Yang
author_facet	Han Yu Chunyan Miao Cyril Leung Yiqiang Chen Simon Fauvel Victor R. Lesser Qiang Yang
author_sort	Han Yu
title	Mitigating Herding in Hierarchical Crowdsourcing Networks
title_short	Mitigating Herding in Hierarchical Crowdsourcing Networks
title_full	Mitigating Herding in Hierarchical Crowdsourcing Networks
title_fullStr	Mitigating Herding in Hierarchical Crowdsourcing Networks
title_full_unstemmed	Mitigating Herding in Hierarchical Crowdsourcing Networks
title_sort	mitigating herding in hierarchical crowdsourcing networks
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/3a2f8306e4384051a670ddd7ab355e30
work_keys_str_mv	AT hanyu mitigatingherdinginhierarchicalcrowdsourcingnetworks AT chunyanmiao mitigatingherdinginhierarchicalcrowdsourcingnetworks AT cyrilleung mitigatingherdinginhierarchicalcrowdsourcingnetworks AT yiqiangchen mitigatingherdinginhierarchicalcrowdsourcingnetworks AT simonfauvel mitigatingherdinginhierarchicalcrowdsourcingnetworks AT victorrlesser mitigatingherdinginhierarchicalcrowdsourcingnetworks AT qiangyang mitigatingherdinginhierarchicalcrowdsourcingnetworks
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Mitigating Herding in Hierarchical Crowdsourcing Networks

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