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

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Abstract

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.

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Acknowledgments

This work was supported by the JST CREST fund (Grant Number: JPMJCR15E1), Japan. It is also supported partially by the Natural Science Foundation (Grant#: 61963026). We would like to thank all project team members for their contribution in the studies and the experiments. We are grateful for all participants at Ito-Lab, Nagoya Institute of Technology in online discussion on CBR application to COLLAGREE.

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Authors and Affiliations

  1. National Research Council Canada, Ottawa, Ontario, K1A 0R6, Canada

    Chunsheng Yang

  2. Department of Educational Technology, Shanghai Normal University, Shanghai, China

    Chunsheng Yang

  3. Nagoya Institute of Technology, Nagoya, Japan

    Chunsheng Yang

  4. Department of Compute Science, Nagoya Institute of Technology Nagoya, Nagoya, Archi, Japan

    Wen Gu & Takayuki Ito

  5. Department of Automation, Nanchang University, Nanchang, China

    Xiaohua Yang

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  1. Chunsheng Yang
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  2. Wen Gu
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  3. Takayuki Ito
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  4. Xiaohua Yang
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Correspondence to Chunsheng Yang.

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Yang, C., Gu, W., Ito, T. et al. Machine learning-based consensus decision-making support for crowd-scale deliberation. Appl Intell 51, 4762–4773 (2021). https://doi.org/10.1007/s10489-020-02118-z

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