In this research, we present neutrosophic decision-making, which is an extension of the classical decision-making process by expanding the data to cover the non-specific cases ignored by the classical logic, which in fact support the decision-making problem. The lack of information besides its inaccuracy is an important constraint affecting The effectiveness of the decision-making process, and we will rely on the decision tree model, which is one of the most powerful mathematical methods used to analyze many decisionmaking problems, where we extend it according to the neutrosophic logic by adding some indeterminate data (in the absence of probability) or by substituting the classical probabilities with the neutrosophic probabilities (in case of probability). We call this extended model the neutrosophic decision tree, which results in its use to reach the best decision among the available alternatives because it is based on data that is more general and accurate than the classical model.
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[v1] 2018-11-20 06:40:32
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