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Central to the framework is its methodological rigor, which encompasses a comprehensive approach to data processing, including feature extraction and anomaly detection. This ensures decisions are grounded in accurate and up-to-date information. The framework employs a systematic algorithm for generating initial decision plans, which are then dynamically expanded to include co-requisite actions, ensuring comprehensive coverage of necessary interventions. A distinctive feature of the framework is its adaptive feedback mechanism, which refines decision plans based on the outcomes of implemented actions and new data, facilitating continuous improvement and relevance of decision strategies. Furthermore, the paper details the application of advanced visualization techniques, such as Sankey diagrams, to elucidate the flow of information and the interdependencies between actions within the decision-making process. This not only enhances the transparency and interpretability of decisions but also fosters stakeholder engagement and consensus. The versatility of the proposed framework is demonstrated through its applicability to a variety of domains, from emergency response planning to business strategy development, showcasing its potential to significantly improve decision-making practices. Through a meticulous examination of the framework's methodology and its application across diverse scenarios, this paper contributes to the advancement of decision science, oYering a scalable and adaptable solution to the challenges of multi-criteria decision-making under uncertainty. |
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