Abstract
Determination of associated weight vectors for an ordered weighted averaging (OWA) operator is always a crucial issue, and for this purpose, several weight vector determination models have been introduced in the literature. In this article, we propose a new biparametric OWA operator, called a “beta-Bézier OWA operator”. The beta-Bézier OWA operator provides an infinite number of weight vectors for a fixed degree of orness. The orness value of this class of OWA operators is its most essential characteristic, which always depends on one of its parameters, and can be prefixed according to the decision-maker choice and also irrespective of the number of arguments aggregated. The optimum OWA weight vector can be calculated without solving a complicated optimization problem. It becomes the same as several other OWA operators at particular values of parameters. The maximum Bayesian entropy OWA operator weight vector is measured with use of the proposed OWA operator for a given level of optimism/pessimism (orness). It is also compared with many other OWA operators.
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