An Improved Uncertainty Measure Theory Based on Game Theory Weighting

Abstract EN

In the application of uncertainty measure theory, the determination method of index weight mainly includes the subjective weight determination method and the objective weight determination method.

The subjective weight determination method has the disadvantages affected by the subjective preference of the decision-maker.

The objective weight determination method often ignores the participation degree of the decision-maker, and when using the uncertainty measure evaluation model to perform multi-index classification evaluation, the credible degree recognition criterion is often used as the attribute recognition of the object to be measured, because the credible degree is taken by the subjective people, and the different values of different people have a great influence on the evaluation results.

In order to solve the above problems in the uncertainty measure theory, this paper used the combination weighting of game theory to determine the optimal weight.

At the same time, the credible degree recognition criterion was improved on the basis of the concept of minimum uncertainty measure distance, and a game theory-improved uncertainty measure optimization model was proposed.

Finally, the validity of the model was proven by a case.