Algorithmic Management for Improving Collective Productivity in Crowdsourcing

doi:10.1038/s41598-017-12757-x

	Algorithmic Management for Improving Collective Productivity in Crowdsourcing
	Yu, Han 1; Miao, Chunyan 1,2; Chen, Yiqiang 3,4; Fauvel, Simon 1; Li, Xiaoming 5; Lesser, Victor R.6
	2017-10-02
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	7 页码:11
摘要	Crowdsourcing systems are complex not only because of the huge number of potential strategies for assigning workers to tasks, but also due to the dynamic characteristics associated with workers. Maximizing social welfare in such situations is known to be NP-hard. To address these fundamental challenges, we propose the surprise-minimization-value-maximization (SMVM) approach. By analysing typical crowdsourcing system dynamics, we established a simple and novel worker desirability index (WDI) jointly considering the effect of each worker's reputation, workload and motivation to work on collective productivity. Through evaluating workers' WDI values, SMVM influences individual workers in real time about courses of action which can benefit the workers and lead to high collective productivity. Solutions can be produced in polynomial time and are proven to be asymptotically bounded by a theoretical optimal solution. High resolution simulations based on a real-world dataset demonstrate that SMVM significantly outperforms state-of-the-art approaches. A large-scale 3-year empirical study involving 1,144 participants in over 9,000 sessions shows that SMVM outperforms human task delegation decisions over 80% of the time under common workload conditions. The approach and results can help engineer highly scalable data-driven algorithmic management decision support systems for crowdsourcing.
DOI	10.1038/s41598-017-12757-x
收录类别	SCI
语种	英语
资助项目	National Research Foundation ; Prime Minister's Office, Singapore under its IDM Futures Funding Initiativ ; Lee Kuan Yew Post-Doctoral Fellowship Grant ; Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project)[MOH/NIC/COG04/2017] ; NTU-PKU Joint Research Institute ; Ng Teng Fong Charitable Foundation ; National Natural Science Foundation of China (NSFC)[61572471] ; National Natural Science Foundation of China (NSFC)[61502456] ; National Natural Science Foundation of China (NSFC)[61572004]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000412050100062
出版者	NATURE PUBLISHING GROUP
引用统计	被引频次：27[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/6792
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Yu, Han; Miao, Chunyan; Chen, Yiqiang
作者单位	1.Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore 639798, Singapore 2.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Peking Univ, Inst Network Comp & Informat Syst, Beijing 100871, Peoples R China 6.Univ Massachusetts, Sch Comp Sci, Amherst, MA 01002 USA
推荐引用方式 GB/T 7714	Yu, Han,Miao, Chunyan,Chen, Yiqiang,et al. Algorithmic Management for Improving Collective Productivity in Crowdsourcing[J]. SCIENTIFIC REPORTS,2017,7:11.
APA	Yu, Han,Miao, Chunyan,Chen, Yiqiang,Fauvel, Simon,Li, Xiaoming,&Lesser, Victor R..(2017).Algorithmic Management for Improving Collective Productivity in Crowdsourcing.SCIENTIFIC REPORTS,7,11.
MLA	Yu, Han,et al."Algorithmic Management for Improving Collective Productivity in Crowdsourcing".SCIENTIFIC REPORTS 7(2017):11.