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A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables

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  • Chen, Chen-Tung
  • Chiu, Yen-Ting

Abstract

A fuzzy cognitive map (FCM) is an analysis tool that uses a graph structure to show the causal relationships of influence factors in a decision-making system. During the decision-making process, it is reasonable for experts to use linguistic variables to express their subjective opinions. However, few studies have discussed methods for aggregating the linguistic opinions of experts to reach a group consensus in FCM. In addition, the interaction weights among the factors in FCM will usually change over time in a real environment. Therefore, we applied the learning algorithm to adjust the interaction weights among the factors in the steps of FCM, after which we proposed a dynamic fuzzy cognitive map model with group consensus based on the linguistic evaluations in this study. Finally, we presented a case study using the proposed model to illustrate the development possibility of the Internet of Things (IoT) industry in Taiwan. According to the analysis results, we found that the development prospects for the IoT industry in Taiwan are optimistic. The four key factors for IoT industry development were found to be the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment and cross-platform possibility.

Suggested Citation

  • Chen, Chen-Tung & Chiu, Yen-Ting, 2021. "A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:tefoso:v:171:y:2021:i:c:s0040162521003802
    DOI: 10.1016/j.techfore.2021.120948
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    References listed on IDEAS

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    1. Pappas, Nikolaos & Caputo, Andrea & Pellegrini, Massimiliano Matteo & Marzi, Giacomo & Michopoulou, Eleni, 2021. "The complexity of decision-making processes and IoT adoption in accommodation SMEs," Journal of Business Research, Elsevier, vol. 131(C), pages 573-583.
    2. Maritza Satama & Eva Iglesias, 2020. "Fuzzy Cognitive Map Clustering to Assess Local Knowledge of Ecosystem Conservation in Ecuador," Sustainability, MDPI, vol. 12(6), pages 1-26, March.
    3. Amirkhani, Abdollah & Papageorgiou, Elpiniki I. & Mosavi, Mohammad R. & Mohammadi, Karim, 2018. "A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 562-582.
    4. Sterman, J.D., 2006. "Learning from evidence in a complex world," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 505-514.
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    Cited by:

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    2. Kunruthai Meechang & Kenji Watanabe, 2023. "Modeling to Achieve Area Business Continuity Management Implementation via a Fuzzy Cognitive Map," Sustainability, MDPI, vol. 15(18), pages 1-22, September.

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