Shahzaib Ashraf , Chiranjibe Jana , Wania Iqbal , Muhammet Deveci
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Regret-based domination three-way decision-making model with circular spherical fuzzy Mahalanobis distance
This article proposes a revolutionary three-way multi-attribute model in response to the increased complexity and risk in decision-making processes. This model combines three-way decision rules with the influential behavioral theories of regret and prospect. To address uncertainties in evaluation data, a circular spherical fuzzy set is seamlessly integrated into the information system, noting that this method can also be smoothly applied to disc spherical fuzzy sets (D-SFSs). Moreover, a pioneering method utilizing correlation coefficients and variances is introduced for attribute weighting. The conventional Mahalanobis distance is extended to a circular, spherical, fuzzy Mahalanobis distance. A novel approach is developed to estimate conditional probabilities based on the distinct regret-based dominating and rejoice-based dominating relations in circular spherical fuzzy sets (C-SFSs). Additionally, three scoring systems based on prospect theory yield despondent, expectant, and balanced decision-making strategies and provide priceless insights into various decision-maker mindsets. Furthermore, to illustrate the practical application of these methods with D-SFSs information, an example has been solved utilizing D-SFSs.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.