This Reprint brings together recent advances in uncertainty-aware decision making, highlighting how entropy-based methods, fuzzy and rough set formalisms, cloud-model reasoning, and distributed learning architectures are reshaping analytical practice across scientific and technical domains. The collected contributions illustrate a clear shift toward integrated uncertainty pipelines that combine robustness, interpretability, and privacy awareness in decision support systems. They address challenges such as heterogeneous information fusion, instability of classical entropy weights, conflict management in distributed environments, and the need for transparent, explainable mechanisms that operate reliably under noisy, incomplete, or adversarial data. By presenting applications spanning medicine, cybersecurity, environmental assessment, multi-criteria evaluation, and coalition-based classification, this Reprint demonstrates how treating uncertainty as structured information enables more resilient, adaptive, and trustworthy decision processes.
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