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In decision making problems under uncertainty, Mean Variance Model (MVM) consistent with Expected Utility Theory (EUT) plays an important role in ranking preferences for various alternative options. Despite its wide use, this model is appropriate only when random variables representing the alternative options are normally distributed and the utility function to be maximized is quadratic; both are undesirable properties to be satisfied with actual applications. This book provides new approaches for Mean Variance Model based on cumulative functions using simulation. It introduces an integrated preference- based AHP model that combines the two modified approaches, namely MVM and AHP. These models can reduce the deficiency of the existing models to solve large-scale decision problems, along with applications to real-world disputes, specifically in financial investment. From the contents: Concepts and definitions MVM consistent with EUT AHP and new algorithms Conceptual framework for an integrated preference - based AHP model The practical implementation of an AHP-based model.
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