<![CDATA[This research proposes a foundational model for data-driven decision systems based on probabilistic preference structures, addressing the limitations of traditional deterministic and utility-based approaches. The model integrates probability theory, Bayesian inference, and decision theory to represent preferences as flexible probability distributions capable of capturing uncertainty, partial orderings, and multi-attribute trade-offs. A set of novel algorithms is introduced for learning and estimating latent probabilistic preferences from noisy, incomplete, and heterogeneous data sources. These learned preference structures are embedded within an optimization framework that combines Bayesian updating with Markov decision processes, enabling the system to generate optimal decisions under uncertainty. Experimental evaluations conducted across synthetic and real-world datasets demonstrate significant improvements in accuracy, robustness, stability, and decision quality compared to existing preference modeling methods. The unified framework also enhances explainability by quantifying uncertainty and providing interpretable probabilistic outputs. The research makes theoretical contributions by establishing a mathematical ontology for probabilistic preferences, methodological contributions through the development of scalable inference and decision algorithms, and practical contributions by enabling reliable decision-making in environments characterized by inconsistent or probabilistic data. Overall, the results validate the proposed framework as a comprehensive and flexible foundation for next-generation intelligent decision systems, offering improved adaptability, reliability, and transparency in complex real-world applications.]]>
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