The current research landscape in the field of preference and decision-making models is witnessing significant advancements, particularly in the areas of game theory and computational efficiency. Researchers are increasingly focusing on developing algorithms that can handle complex, hierarchical decision structures, ensuring both consistency and optimality in decision outcomes. A notable trend is the integration of game-theoretic approaches with computational methods to solve problems in security control selection and agricultural cooperative strategies, demonstrating the versatility and robustness of these models. Additionally, there is a strong emphasis on the enforcement of coherent and minimal sets of constraints in mathematical data models, which is crucial for maintaining the integrity and efficiency of database management systems. The field is also making strides in preference inference, with new algorithms that promise polynomial-time solutions for consistency and optimality checks, significantly enhancing the practical applicability of these models. Overall, the direction of the field is towards more efficient, robust, and versatile computational tools that can handle increasingly complex decision-making scenarios.