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Algorithmic Management for Improving Collective Productivity in Crowdsourcing

Overview of attention for article published in Scientific Reports, October 2017
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Title
Algorithmic Management for Improving Collective Productivity in Crowdsourcing
Published in
Scientific Reports, October 2017
DOI 10.1038/s41598-017-12757-x
Pubmed ID
Authors

Han Yu, Chunyan Miao, Yiqiang Chen, Simon Fauvel, Xiaoming Li, Victor R. Lesser

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 18%
Researcher 10 15%
Student > Ph. D. Student 8 12%
Student > Doctoral Student 6 9%
Lecturer 5 8%
Other 11 17%
Unknown 14 21%
Readers by discipline Count As %
Business, Management and Accounting 15 23%
Computer Science 11 17%
Social Sciences 7 11%
Engineering 5 8%
Arts and Humanities 3 5%
Other 8 12%
Unknown 17 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 October 2017.
All research outputs
#15,480,316
of 23,003,906 outputs
Outputs from Scientific Reports
#78,444
of 124,216 outputs
Outputs of similar age
#202,147
of 322,939 outputs
Outputs of similar age from Scientific Reports
#3,178
of 5,327 outputs
Altmetric has tracked 23,003,906 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 124,216 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,939 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,327 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.