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Dynamic cloud model based on decision field theory

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A Correction to this article was published on 11 April 2025

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Abstract

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.

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The authors confirm that the data supporting the findings of this study are accessible within the article.

Change history

  • 11 April 2025

    “The original online version of this article was revised:” In this article the author’s name Anjali was incorrectly written as Anjali Anjali.

  • 11 April 2025

    A Correction to this paper has been published: https://doi.org/10.1007/s11227-025-07286-8

Notes

  1. The cloud Hamming distance \(d(C_a, C_b), a,b \in \mathbb {N}\) satisfies the following key properties:

    • Symmetry: \(d(C_a, C_b) = d(C_b, C_a)\).

    • Law of Indiscernibility: \(d(C_a, C_b) = 0\) if and only if \(C_a = C_b\).

    • Triangle Inequality: \(d(C_a, C_c) \le d(C_a, C_b) + d(C_b, C_c)\) for any clouds \(C_a\), \(C_b\), and \(C_c\).

    • Non-Negativity: \(d(C_a, C_b) \ge 0\) for all \(a\) and \(b\).

  2. https://www.kaggle.com/datasets/ydalat/lifestyle-and-wellbeing-data

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Authors and Affiliations

  1. Department of Applied Mathematics, Delhi Technological University, New Delhi, Delhi, 110042, India

    Anjali & Anjana Gupta

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  1. Anjali
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A: conception, methodology, composition, editing and revision; AG: conception, methodology, formal analysis, and review.

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Correspondence to Anjana Gupta.

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“The original online version of this article was revised:” In this article the author’s name Anjali was incorrectly written as Anjali Anjali.

Appendix

Appendix

Table 12 presents the descriptive statistics for each attribute in the Lifestyle and Well-being dataset.

Table 12 Descriptive Statistics of Lifestyle & Well-being Attributes
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Algorithm 4 outlines the pseudocode for generating dynamic clouds, assessing alternative preferences with dynamic weights from a custom ARIMA model, and calculating the likelihood of selecting one alternative over another.

Algorithm 4
figure d

Dynamic Cloud Model for Analyzing Preferences under uncertainty Using Decision Field Theory

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Anjali, Gupta, A. Dynamic cloud model based on decision field theory. J Supercomput 81, 620 (2025). https://doi.org/10.1007/s11227-025-06989-2

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