Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89483
Title: Algorithmic management for improving collective productivity in crowdsourcing
Authors: Yu, Han
Miao, Chunyan
Chen, Yiqiang
Fauvel, Simon
Li, Xiaoming
Lesser, Victor R.
Keywords: Algorithmic Management
Major Clinical Study
Issue Date: 2017
Source: Yu, H., Miao, C., Chen, Y., Fauvel, S., Li, X., & Lesser, V. R. (2017). Algorithmic Management for Improving Collective Productivity in Crowdsourcing. Scientific Reports, 7(1), 12541-.
Series/Report no.: Scientific Reports
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.
URI: https://hdl.handle.net/10356/89483
http://hdl.handle.net/10220/44976
ISSN: 2045-2322
DOI: http://dx.doi.org/10.1038/s41598-017-12757-x
Rights: © 2017 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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