Machine learning-based consensus decision-making support for crowd-scale deliberation

With the rapid development of Internet, the online discussion system or social democratic system has become an important and effective vehicle for group decision-making support since it can continue collecting the opinions from the public at anytime. To reach a consensus in crowd-scale deliberation,...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2021-07, Vol.51 (7), p.4762-4773
Main Authors: Yang, Chunsheng, Gu, Wen, Ito, Takayuki, Yang, Xiaohua
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Decision making
Human performance
Machine learning
Machines
Manufacturing
Mechanical Engineering
Processes
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Summary:With the rapid development of Internet, the online discussion system or social democratic system has become an important and effective vehicle for group decision-making support since it can continue collecting the opinions from the public at anytime. To reach a consensus in crowd-scale deliberation, the existing online discussion systems require an experienced human facilitator to navigate and guild the discussion. When human facilitator performs the required facilitation there are several issues such as heavy burden on decision-making, the 24/7 online facilitation, bias on the social issues, etc. To address these issues it is necessary and inevitable to explore intelligent facilitation. For this purpose, we propose a novel machine learning-based method for smart facilitation, in particular the intelligent consensus decision-making support (CDMS) for crowd-scale deliberation. After presenting an overview of the crowd-scale deliberation and the COLLAGREE, the paper details the proposed approach, a machine learning-based framework for CDMS in crowd-scale deliberation. To validate the developed methods the offline evaluation experiments were conducted with the online discussion platform, COLLAGREE. The preliminary experimental results obtained from offline validation demonstrated the feasibility and usefulness of the developed machine learning-based methods for CDMS.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-02118-z