Fusion and automatic ranking of fuzzy outcome preferences information in hypergame models

Hypergame is a typical framework to deal with decision situations with multiple conflicting players who perceive the conflict differently and subjectively. In the face of opponent players´ different outcome preferences information over the outcome space perceived by different decision makers using fuzzy linguistic values, a concept of fuzzy relational degree between different fuzzy numbers sequences is first presented in this paper, and based on fuzzy relational degree analysis, the weight of each decision maker can be evaluated and the integrated fuzzy outcome preferences can be obtained. Then, a multi-layer feedforward artificial neural network (ANN) is designed and trained to learn the criterions of comparing trapezoidal fuzzy numbers. Using the trained ANN, the integrated fuzzy outcome preferences can be ranked automatically. At last, a numerical example is provided to illustrate the proposed method.