Two novel approaches that reduce the effectiveness of the decision maker in decision making under uncertainty environments

Unlike other mathematical models, soft set theory provides a parameterization tool contribution. However, in this theory, since membership degrees are expressed as $0$ and $1$, for $(0, 1)$, we cannot determine whether any object belongs to a parameter or not. Researchers have tried to overcome this situation by ensuring that the decision maker expresses these values. However, we cannot know the accuracy of the data provided to us by the decision maker. Therefore, in this study, we introduced the concepts of relational membership function, relational non-membership function, inverse relational membership function and inverse relational non-membership function and examined the related properties of these concepts. Then, we propose two new approaches so that uncertainty can be expressed in an ideal way and can be used in decision-making. Finally, the approaches given and some of the important approaches in the literature are compared and analyzed.