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<p>Real-world applications are characterized by uncertainties, with randomness and fuzziness being the significant challenges inherent in human cognition. The Cloud Model (CM) synthesizes these uncertainties, enabling the transformation between qualitative and quantitative instantiations. Integrating CM with Decision Field Theory (DFT) is essential for managing the complexities of dynamic and probabilistic decision-making. Our research introduces a novel Dynamic Cloud Model based on Decision Field Theory (DCM-DFT). It incorporates an innovative methodology for creating dynamic clouds by capturing initial uncertainty using cloud descriptors. We built a custom time series model to compute attention weights for forecasted periods, effectively managing the full spectrum of uncertainty fluctuations in a decision maker’s dynamic mental state. We demonstrate DCM-DFT’s practical implementation using the Lifestyle and Wellbeing real-time dataset, showcasing fluctuations in decision ranking of alternatives over time. A comparative study exhibits DCM-DFT’s enhanced performance over traditional approaches, significantly improving decision-making preferences in dynamic and uncertain environments.</p>

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Dynamic cloud model based on decision field theory

  • 期刊论文

  • 发表日期：

    2025年3月14日

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  • Anjali,
  • Anjana Gupta

摘要

Real-world applications are characterized by uncertainties, with randomness and fuzziness being the significant challenges inherent in human cognition. The Cloud Model (CM) synthesizes these uncertainties, enabling the transformation between qualitative and quantitative instantiations. Integrating CM with Decision Field Theory (DFT) is essential for managing the complexities of dynamic and probabilistic decision-making. Our research introduces a novel Dynamic Cloud Model based on Decision Field Theory (DCM-DFT). It incorporates an innovative methodology for creating dynamic clouds by capturing initial uncertainty using cloud descriptors. We built a custom time series model to compute attention weights for forecasted periods, effectively managing the full spectrum of uncertainty fluctuations in a decision maker’s dynamic mental state. We demonstrate DCM-DFT’s practical implementation using the Lifestyle and Wellbeing real-time dataset, showcasing fluctuations in decision ranking of alternatives over time. A comparative study exhibits DCM-DFT’s enhanced performance over traditional approaches, significantly improving decision-making preferences in dynamic and uncertain environments.