Decision Making with Dempster-Shafer Theory Using Fuzzy Induced Aggregation Operators

We develop a new approach for decision making with Dempster-Shafer theory of evidence where the available information is uncertain and it can be assessed with fuzzy numbers. With this approach, we are able to represent the problem without losing relevant information, so the decision maker knows exactly which are the different alternatives and their consequences. For doing so, we suggest the use of different types of fuzzy induced aggregation operators in the problem. Then, we can aggregate the information considering all the different scenarios that could happen in the analysis. As a result, we get new types of fuzzy induced aggregation operators such as the belief structure – fuzzy induced ordered weighted averaging (BS-FIOWA) and the belief structure – fuzzy induced hybrid averaging (BS-FIHA) operator. We study some of their main properties. We further generalize this approach by using fuzzy induced generalized aggregation operators. We also develop an application of the new approach in a financial decision making problem about selection of financial strategies.

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