Abstract
To integrate incentives into the information aggregation process in decision making, we propose a new type of aggregation operator, denominated as the quantile induced heavy ordered weighted averaging (QI-HOWA) operator in this paper. A primary characteristic of this type of operator is that the quantile variable can be used not only to measure the relative performance of alternatives but also to facilitate the incentive preference expression of the decision maker. We further provide a calculation technology of the QI-HOWA weights, in which various incentive preferences of the decision maker can be considered through parameter adjustment. In addition, we discuss certain properties of the QI-HOWA operator and note the extent to which they are effective. Finally, a numerical example regarding the selection of optimal alternatives by incentive measures is provided, and the aggregations are compared with those of ordered weighted averaging and unweighted averaging operators to illustrate the validity of the QI-HOWA operator.
Published Version
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