Some properties of the weighted OWA operator.

Based on the researches on ordered weighted average (OWA) operator, the weighted OWA operator (WOWA) and especially the quantifier guided aggregation method, with the generating function representation of regular increasing monotone (RIM) quantifier technique, we discuss the properties of WOWA operator with RIM quantifier in the respect of orness. With the continuous OWA and WOWA ideas recently proposed by Yager, an improvement on the continuous OWA and WOWA operator is proposed. The properties of WOWA are also extended from discrete to the continuous case. Based on these properties, two families of parameterized RIM quantifiers for WOWA operator are proposed, which have exponential generating function and piecewise linear generating function respectively. One interesting property of these two kinds of RIM quantifiers is that for any aggregated set (or variable) under any weighted (distribution) function, the aggregation values are always consistent with the orness (optimistic) levels, so they can be used to represent the decision maker's preference, and we can get the preference value of fuzzy sets or random variables with the orness level of RIM quantifier as their control parameter.     

Preference solutions of probability decision making with rim quantifiers

This article extends the quantifier‐guided aggregation method to include probabilistic information. A general framework for the preference solution of decision making under an uncertainty problem is proposed, which can include decision making under ignorance and decision making under risk methods as special cases with some specific preference parameters. Almost all the properties, especially the monotonicity property, are kept in this general form. With the generating function representation of the Regular Increasing Monotone (RIM) quantifier, some properties of the RIM quantifier are discussed. A parameterized RIM quantifier to represent the valuation preference for probabilistic decision making is proposed. Then the risk attitude representation method is integrated in this quantifier‐guided probabilistic decision making model to make it a general form of decision making under uncertainty. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1253–1271, 2005.     

Some quantifier functions from weighting functions with constant value of orness.

The quantifier-guided aggregation is used for aggregating the multiple-criteria input. Therefore, the selection of appropriate quantifiers is crucial in multicriteria aggregation since the weights for the aggregation are generated from the selected quantifier. Since Yager proposed a method for obtaining the ordered weighted averaging (OWA) vector via the three relative quantifiers used for the quantifier-guided aggregation, limited efforts have been devoted to developing new quantifiers that are suitable for use in multicriteria aggregation. In this correspondence, we propose some new quantifier functions that are based on the weighting functions characterized by showing a constant value of orness independent of the number of criteria aggregated. The proposed regular increasing monotone and regular decreasing monotone quantifiers produce the same orness as the weighting functions from which each quantifier function originates. Further, the quantifier orness rapidly converges into the value of orness of the weighting functions having a constant value of orness. This result indicates that a quantifier-guided OWA aggregation will result in a similar aggregate in case the number of criteria is not too small.     

Dynamic weights allocation according to uncertain evaluation information

Weights allocation methods are critical in Multi-Criteria Decision Making. Given numerical importances for each involved criterion, direct normalizing those numerical importances to obtain weights for those criteria is plain, lack of flexibility, and thus cannot well model some more types of subjective preferences of different decision makers like Dominance Strength as defined in this study. We show that concave RIM quantifier Q based OWA weights allocation method can well handle and model such preference. However, in real decision making those numerical importances are very often embodied by uncertain information such as independent random variables with discrete or continuous distributions, statistic information and interval numbers. In any of those circumstances, simple RIM quantifier Q based OWA weights allocation cannot work. Therefore, in this study, we will propose some special dynamic weights allocation methods to gradually allocate weights and accumulate allocated parts to each criterion, and finally, obtain a total weights collection. When the uncertain numerical importances become equivalent to general real numbers, the method automatically degenerates into general RIM quantifier based OWA weights allocation. The innovative weight allocations have discrete and continuous versions: the former can be well programmed while the latter has neat and succinct mathematical expression. The method can also be widely used in many other applications like some economic problems including investment quota allocation for one’s favorite stocks, and the dynamic OWA aggregation for interval numbers.     

Similarity classifier with ordered weighted averaging operators

In this article we extend the similarity classifier to cover also ordered weighted averaging (OWA) operators. Earlier, similarity classifier was mainly used with generalized mean operator, but in this article we extend this aggregation process to cover more general OWA operators. With OWA operators we concentrate on linguistic quantifier guided aggregation where several different quantifiers are studied and on how they best suite for the similarity classifier. Our proposed method is applied to real world medical data sets which are new thyroid, hypothyroid, lymphography and hepatitis data sets. Results are very promising and show improvement compared to the earlier used generalized mean operator. In this article we will show that by using OWA operators instead of generalized mean, we can improve classification accuracy with chosen data sets.     

Nonmonotonic OWA operators

 The basic properties of the Ordered Weighted Averaging (OWA) operator are recalled. The role of these operators in the formulation of multi-criteria decision functions, using the concept of quantifier guided aggregation, is discussed. An extended class of OWA operators, one based upon a relaxation of the requirements on the OWA operators, is introduced. This relaxation allows us to consider a new branch of OWA operators, NOMOWA operators, which have negative weights and which exhibit nonmonotonicity. Some special cases of these operators are discussed and then we investigate the role of these nonmonotonic operators in the formulation of multi-criteria decision functions.     

基于语言型混合算子的模糊信息聚合方法

在多属性决策过程中经常会用到聚合算子,有序加权平均聚合(OWA)算子是最常用的聚合算子之一,通常用于聚合确切的数值.然而,现实世界部分信息的不确定性以及决策者对一些信息的模糊性,使得部分信息不能用确切的数值表示,从而导致OWA算子及其扩展算子向着多元化发展.对此,给出一种语言型混合有序加权平均聚合(LHOWA)算子,同时研究该算子所应具备的一些基本性质,并给出一种基于该算子的语言型信息聚合方法,用于多属性决策过程中模糊信息的聚合.最后,通过一个煤矿安全评价的算例对所提出方法的优越性进行了验证.     

Parameterized additive neat OWA operators with different orness levels

The article proposes an extension of the BADD OWA operator—ANOWA (additive neat OWA) operator—and defines its orness measure. Some properties of the weighting function associated with orness level are analyzed. Then two special classes of ANOWA operator with maximum entropy and minimum variance are proposed, and the orness of the BADD OWA operator is discussed. For a given orness level, these ANOWA operators can be uniquely determined. Their aggregation values for any aggregation elements set always monotonically increase with their orness levels. Therefore they can be used as a parameterized aggregation method with orness as its control parameter and to represent the decision maker's preference. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1045–1072, 2006.     

Weighted‐selective aggregated majority‐OWA operator and its application in linguistic group decision making

This paper focuses on the aggregation operations in the group decision‐making model based on the concept of majority opinion. The weighted‐selective aggregated majority‐OWA (WSAM‐OWA) operator is proposed as an extension of the SAM‐OWA operator, where the reliability of information sources is considered in the formulation. The WSAM‐OWA operator is generalized to the quantified WSAM‐OWA operator by including the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM‐IOWA operator, with an ordering step, is introduced to the individual fusion strategy. The proposed aggregation operators are then implemented for the case of alternative scheme of heterogeneous group decision analysis. The heterogeneous group includes the consensus of experts with respect to each specific criterion. The exhaustive multicriteria group decision‐making model under the linguistic domain, which consists of two‐stage aggregation processes, is developed in order to fuse the experts’ judgments and to aggregate the criteria. The model provides greater flexibility when analyzing the decision alternatives with a tolerance that considers the majority of experts and the attitudinal character of experts. A selection of investment problem is given to demonstrate the applicability of the developed model.     

Issues in the practical use of the OWA operators in fuzzy querying

Flexible querying of the relational databases is considered. The applicability of some non-standard, mainly linguistic quantifier driven aggregation, and via Yager’s ordered weighted averaging (OWA) operators in particular, is shown. Their handling is studied with a special emphasis on the selection and tuning of the OWA operator that is appropriate for the user needs. We start with an OWA operator and intend to tune it to increase its ORness, but keeping the changes as limited as possible, or to preserve consistency of the changes. These tasks are defined as optimization problems. The discussion is illustrated on the example of the authors’ FQUERY for Access system.     

Elliptical distribution-based weight-determining method for ordered weighted averaging operators

The ordered weighted averaging (OWA) operators play a crucial role in aggregating multiple criteria evaluations into an overall assessment supporting the decision makers’ choice. One key point steps is to determine the associated weights. In this paper, we first briefly review some main methods for determining the weights by using distribution functions. Then we propose a new approach for determining OWA weights by using the regular increasing monotone quantifier. Motivated by the idea of normal distribution-based method to determine the OWA weights, we develop a method based on elliptical distributions for determining the OWA weights, and some of its desirable properties have been investigated.     

A New Ordered Weighted Averaging Operator to Obtain the Associated Weights Based on the Principle of Least Mean Square Errors

Determining OWA (ordered weighted averaging) weights has received more and more attention since the appearance of the OWA operator. Based on the principle of least mean squared errors, a new parametric OWA operator is proposed to obtain its associated weights. In coordination with fuzzy inference and a few of judgments on weights provided by decision makers (DMs), the new operator is carefully designed to avoid some problems of the existing ones, such as uncertainty in determining an objective function and the measure of orness, etc. Some properties of the problem are discussed to guarantee reliability in theory. A real‐life problem and two simulation experiments are performed to investigate its efficiency. All results show that the proposed operator can be a useful tool to express DMs’ preference information flexibly and objectively.     

An overview of methods for determining OWA weights

The ordered weighted aggregation (OWA) operator has received more and more attention since its appearance. One key point in the OWA operator is to determine its associated weights. In this article, I first briefly review existing main methods for determining the weights associated with the OWA operator, and then, motivated by the idea of normal distribution, I develop a novel practical method for obtaining the OWA weights, which is distinctly different from the existing ones. The method can relieve the influence of unfair arguments on the decision results by weighting these arguments with small values. Some of its desirable properties have also been investigated. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 843–865, 2005.     

Monotonic argument‐dependent OWA operators

The ordered weighted averaging (OWA) operator introduced by Yager is one of the most popular aggregation technique. In this paper, we develop two kinds of argument‐dependent OWA (DOWA) operators including the pessimistic‐dependent OWA (PE‐DOWA) operator and optimistic‐dependent OWA (OP‐DOWA) operator, that point out that the PE‐DOWA operator is decreasing and the OP‐DOWA operator is increasing, and investigate some properties of our proposed monotonic DOWA operators in detail. Furthermore, we introduce the concept of original function in which a gradient vector generates the weights of the PE‐DOWA and OP‐DOWA operators. Meanwhile, we propose two classes of original functions including summing‐type original function and multiplying‐type original function and investigate the sufficient monotonic conditions for the DOWA operators generated by the original functions. Finally, we discuss the characteristics and properties of our proposed DOWA operators in detail and use a numerical example to illustrate the flexibility of our proposed operators.     

A majority model in group decision making using QMA–OWA operators

Group decision‐making problems are situations where a number of experts work in a decision process to obtain a final value that is representative of the global opinion. One of the main problems in this context is to design aggregation operators that take into account the individual opinions of the decision makers. One of the most important operators used for synthesizing the individual opinions in a representative value of majority in the OWA operator, where the majority concept used aggregation processes, is modeled using fuzzy logic and linguistic quantifiers. In this work the semantic of majority used in OWA operators is analyzed, and it is shown how its application in group decision‐making problems does not produce representative results of the concept expressed by the quantifier. To solve this type of problem, two aggregation operators, QMA–OWA, are proposed that use two quantification strategies and a quantified normalization process to model the semantic of the linguistic quantifiers in the group decision‐making process. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 193–208, 2006.     

On the properties of OWA operator weights functions with constant level of orness

The result of aggregation performed by the ordered weighted averaging (OWA) operator heavily depends upon the weighting vector used. A number of methods have been presented for obtaining the associated weights. In this paper, we present analytic forms of OWA operator weighting functions, each of which has properties of rank-based weights and a constant level of orness, irrespective of the number of objectives considered. These analytic forms provide significant advantages for generating the OWA weights over previously reported methods. First, the OWA weights can be efficiently generated by using proposed weighting functions without solving a complicated mathematical program. Moreover, convex combinations of these specific OWA operators can be used to generate the OWA operators with any predefined values of orness once specific values of orness are a priori stated by the decision maker. Those weights have a property of constant level of orness as well. Finally, the OWA weights generated at a predefined value of orness make almost no numerical difference with maximum entropy OWA weights in terms of dispersion.     

INTUITIONISTIC UNCERTAIN LINGUISTIC WEIGHTED BONFERRONI OWA OPERATOR AND ITS APPLICATION TO MULTIPLE ATTRIBUTE DECISION MAKING

With respect to multiple attribute decision making (MADM) problems, in which attribute values take the form of intuitionistic uncertain linguistic information, a new decision-making method based on the intuitionistic uncertain linguistic weighted Bonferroni OWA operator is developed. First, the score function, accuracy function, and comparative method of the intuitionistic uncertain linguistic numbers are introduced. Then, an intuitionistic uncertain linguistic Bonferroni OWA (IULBOWA) operator and an intuitionistic uncertain linguistic weighted Bonferroni OWA (IULWBOWA) operator are developed. Furthermore, some properties of the IULBOWA and IULWBOWA operators, such as commutativity, idempotency, monotonicity, and boundedness, are discussed. At the same time, some special cases of these operators are analyzed. Based on the IULWBOWA operator, the multiple attribute decision-making method with intuitionistic uncertain linguistic information is proposed. Finally, an illustrative example is given to illustrate the decision-making steps and to demonstrate its practicality and effectiveness.     

Non-dominance and attitudinal prioritisation methods for intuitionistic and interval-valued intuitionistic fuzzy preference relations

A novel intuitionistic fuzzy set (IFS) score function and an intuitionistic fuzzy preference relation (IFPR) quantifier guided non-dominance based prioritisation method are introduced. Based on Yager’s continuous OWA (COWA) operator, the interval-valued intuitionistic fuzzy COWA (IVIF-COWA) operator is defined, and a new attitudinal expected score function for interval-valued intuitionistic fuzzy numbers (IVIFNs) is introduced. The novelty of this attitudinal expected score function is that it allows the comparison of IVIFNs by taking into account of the decision makers’ attitudinal character. Moreover, we show that the new attitudinal expected score function extends: (i) the IFS score function introduced in this paper, which is mathematically equivalent to Chen and Tan’s score function (Chen and Tan, 1994); and (ii) Xu and Chen’s score function for IVIFNs (Xu and Chen, 2007). Using the proposed score functions, a method is developed to construct FPRs from a given IFPR and IVIFPR, respectively. When the hesitancy degree function is null, we prove that the score FPRs coincide with their respective IFPR and IVIFPR. Finally, a ranking sensitivity analysis of the attitudinal expected score function with respect to the attitudinal parameter is provided.     

OWA aggregation with an uncertainty over the arguments

We discuss the OWA aggregation operation and the role the OWA weights play in determining the type of aggregation being performed. We introduce the idea of a weight generating function and describe its use in obtaining the OWA weights. We emphasize the importance of the weight generating function in prescribing the type of aggregation to be performed. We consider the problem of performing a prescribed OWA aggregation in the case when we have a probability distribution over the argument values. We show how we use the weight generating function to enable this type of aggregation. Next we consider the situation when we have a more general measure based uncertainty over the argument values. Here again we show how we can use the weight generating function to aid in performing the prescribed OWA aggregation in the face of this more general type of uncertainty. Finally we look at the task of obtaining a weight generating function from a given set of OWA weights.     

A study of the origin and uses of the ordered weighted geometric operator in multicriteria decision making

The ordered weighted geometric (OWG) operator is an aggregation operator that is based on the ordered weighted averaging (OWA) operator and the geometric mean. Its application in multicriteria decision making (MCDM) under multiplicative preference relations has been presented. Some families of OWG operators have been defined. In this article, we present the origin of the OWG operator and we review its relationship to the OWA operator in MCDM models. We show a study of its use in multiplicative decision‐making models by providing the conditions under which reciprocity and consistency properties are maintained in the aggregation of multiplicative preference relations performed in the selection process. © 2003 Wiley Periodicals, Inc.