Nowadays, decision making problems have increased their complexity and a single decision maker cannot handle these problems, with a more diverse and comprehensive view of them being necessary, which results in group decision making (GDM) schemes. The complexity of GDM problems is often due to their inherent uncertainty that is not solved just by using a group. Consequently, different methodologies has been proposed to handle it, in which, the use of the fuzzy linguistic approach stands out. Among the multiple fuzzy linguistic modeling approaches, Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) information has been recently introduced, which enhances classical linguistic modeling that is based on single terms by providing linguistic expressions in a continuous linguistic domain. Its application to decision making is quite promising, but it is necessary to develop enough operators to accomplish aggregation processes in the decision solving scheme. So far, just a small number of aggregation operators have been defined for ELICIT information. Hence, this paper aims at providing new aggregation operators for ELICIT information by developing novel OWA based operators, such as the Induced OWA (IOWA) operator in order to avoid the OWA operator needs of reordering its arguments, because ELICIT information does not have an inherent order due to its fuzzy representation. Our proposal not only consists of extending the definition of an IOWA operator for ELICIT information with crisp weights, but it is also proposed a type-1 IOWA operator for ELICIT information in which both weights and arguments are fuzzy as well as the use of ELICIT information constructing the order inducing variable to reorder the arguments. Additionally, the use of ELICIT information in GDM demands the ability to manage majority based decisions that are better represented in the IOWA operator by linguistic quantifiers. Hence, a majority-driven GDM process for ELICIT information is proposed, which it is the first proposal for fulfilling the majority solving process for GDM while using ELICIT information. Eventually, an illustrative example and a brief comparative analysis are presented in order to show the performance of the proposal and its feasibility.

中文翻译：

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诱导的OWA运算符，用于通过符号翻译处理扩展的比较语言表达的群体决策

如今，决策问题的复杂性越来越高，一个决策者无法处理这些问题，因此有必要对它们进行更多样化和更全面的了解，从而形成了群体决策（GDM）方案。GDM问题的复杂性通常是由于其固有的不确定性，而不仅仅是使用一组即可解决。因此，已经提出了不同的方法来处理它，其中，模糊语言方法的使用是突出的。在多种模糊语言建模方法中，最近引入了带符号翻译的扩展比较语言表达（ELICIT）信息，该信息通过在连续语言域中提供语言表达来增强基于单项的经典语言建模。它在决策中的应用是很有前途的，但是有必要开发足够的运算符以完成决策解决方案中的聚合过程。到目前为止，仅为ELICIT信息定义了少数聚合运算符。因此，本文旨在通过开发新颖的基于OWA的运算符（例如，诱导OWA（IOWA）运算符）来为ELICIT信息提供新的聚合运算符，从而避免OWA运算符需要对其参数进行重新排序，因为ELICIT信息没有固有的顺序由于其模糊表示。我们的建议不仅包括扩展具有明确权重的IOWA运算符对ELICIT信息的定义，但是，还提出了一种用于ELICIT信息的类型1 IOWA运算符，其中权重和自变量都是模糊的，以及使用ELICIT信息构造顺序诱导变量来对自变量进行重新排序。另外，在GDM中使用ELICIT信息需要具有管理基于多数的决策的能力，这些决策可以通过语言量词在IOWA运算符中更好地表示。因此，提出了针对ELICIT信息的多数驱动GDM过程，这是在使用ELICIT信息时实现针对GDM的多数解决过程的第一个提议。最后，给出了一个说明性的例子和简要的比较分析，以显示该提案的性能及其可行性。在GDM中使用ELICIT信息要求具有管理基于多数的决策的能力，这些决策可以用语言量词在IOWA运算符中更好地表示。因此，提出了针对ELICIT信息的多数驱动GDM过程，这是在使用ELICIT信息时实现针对GDM的多数解决过程的第一个提议。最后，给出了一个说明性的例子和简要的比较分析，以显示该提案的性能及其可行性。在GDM中使用ELICIT信息要求具有管理基于多数的决策的能力，这些决策可以用语言量词在IOWA运算符中更好地表示。因此，提出了针对ELICIT信息的多数驱动GDM过程，这是在使用ELICIT信息时实现针对GDM的多数解决过程的第一个提议。最后，给出了一个说明性的例子和简要的比较分析，以显示该提案的性能及其可行性。