有序分位加权集结算子及在激励评价中的应用

为在信息集结过程中体现指标值的相对发展水平,提出了一种新的集结方法,即有序分位加权集结算子.该算子尤其适用于激励评价问题,其主要特征是用分位数表示指标值的相对发展水平,并且在信息集结过程中融入了决策者不同程度的激励偏好.通过性质分析,发现该算子具有置换不变性、界值性和条件单调性等特征.进一步,以算例的方式分析了该算子在激励评价中的应用问题,发现该算子在信息集结中通过权重加和不等于1的方式能够放大或缩小集结值,从而凸显被评价对象之间的差异,实现激励的目的.

Ordered fractile weighted aggregation operators and the application in incentive evaluation

To express relative development of attribute values in aggregation process, a new type of method, the ordered fractile weighted aggregation (OFWA) operator, is proposed. This type of operator is, especially, suitable for incentive questions. Its main characteristic is that the fractile variable is introduced to measure the degree of which the development of attribute values, and the incentive preference of decision makers is fused into aggregation neatly. By properties analysis, it is found that this operator is commutative, bounded, and monotonic under certain conditions. In addition, a numerical example is applied to the question of incentive evaluation, and it appears that this operator can enlarge or reduce the aggregations by letting the sum of weights not be 1. In this case the different among objects becomes distinct so as to realize the purpose of incentive.