A novel linguistic decision-making method based on the voting model for large-scale linguistic decision making

The notable characteristic of large-scale linguistic decision-making problems is that there are so many decision makers who provide linguistic assessments by using fuzzy linguistic representation models. In real-world applications, fuzzy linguistic terms mean different things for different people, and linguistic assessments based on different linguistic representation models may be simultaneous in the same large-scale linguistic decision-making problems. To this end, a novel linguistic decision-making method based on the voting model is proposed in the paper to deal with multi-linguistic assessments provided by decision makers. In large-scale linguistic decision process, evaluation-based voting is defined and multi-linguistic decision matrix is designed to represent multi-linguistic assessments provided by decision makers by using different linguistic representation models, and properties of the decision matrix are analyzed to show that linguistic assessments based on different linguistic representation models can be simultaneously represented. Based on multi-linguistic decision matrix, a new linguistic decision-making framework is developed to deal with large-scale linguistic decision-making problems with multi-linguistic assessments, in which normalization of multi-linguistic decision matrix and trust degrees of linguistic terms are contained, and more important, based on trust degrees of linguistic terms and 2-tuple fuzzy linguistic aggregation operators, an uniform fusion method of multi-linguistic assessments is proposed to aggregate multi-linguistic assessments of large-scale linguistic decision-making problems. Finally, user experiences of shared bikes, which are a large-scale linguistic decision-making problem in real-world applications, are employed to show the new decision-making framework and the uniform fusion method of multi-linguistic assessments, and furthermore, compared with existing linguistic decision-making methods analyzed in the example, it seems that multi-linguistic decision matrix and the uniform fusion method are useful and effective tools to deal with large-scale linguistic decision-making problems with multi-linguistic assessments.

A Novel Linguistic Decision Making Method Based on the Voting Model for Large-Scale Linguistic Decision making

The notable characteristic of large-scale linguistic decision making problems is that there are so many decision makers provide linguistic assessments according to fuzzy linguistic representation models. In real world applications, fuzzy linguistic terms mean different things for different people, it means that linguistic assessments based on linguistic representation models may be simultaneous in the same large-scale linguistic decision making problems. To this end, a novel linguistic decision making method based on the voting model is proposed in the paper, which can be utilized to deal with large-scale linguistic decision making problems with linguistic assessments based on multi linguistic representation models. In large-scale linguistic decision process, evaluation-based voting is defined and multi linguistic decision matrix is designed to represent multi linguistic assessments provided by many decision makers according to linguistic representation models, properties of the decision matrix are analysed to show that linguistic assessments based on linguistic representation models can be simultaneously represented. Based on multi linguistic decision matrix, a new linguistic decision making framework is developed to deal with large-scale linguistic decision making problems with multi linguistic assessments, in which normalization of multi linguistic decision matrix and trust degrees of linguistic terms are contained, more important, based on trust degrees of linguistic terms and 2-tuple fuzzy linguistic aggregation operators, an uniform fusion method of multi linguistic assessments is proposed to aggregate multi linguistic assessments of large-scale linguistic decision making problems. Finally, user experiences of shared bikes, which is a large-scale linguistic decision making problem in real-world applications, is employed to show the new decision making framework and the uniform fusion method of multi linguistic assessments, furthermore comparison with existing linguistic decision making methods are analyzed in the example, it seems that multi linguistic decision matrix and the uniform fusion method are useful and effective tools to deal with large-scale linguistic decision making problems with multi linguistic assessments.

Extending the linguistic decision suite FLINTSTONES to deal with comparative linguistic expressions with symbolic translation information

Uncertainty is so common in real decision situations that it has given rise to a new decision making approach so-called linguistic decision making, in which such uncertainty is modeled by using linguistic information. Many contributions have been proposed in order to solve LDM problems by following a Computing with Words (CW) approach to obtain linguistic outputs from linguistic premises by emulating the human beings’ reasoning process. Nowadays, there are several LDM models that, together with the complexity of LDM problems, make almost impossible to find a proper solution for these problems without a support tool. FLINTSTONES is a fuzzy decision support system that facilitates the decision process in LDM problems. This software aimed to solve LDM problems by means of the 2-tuple linguistic model, whose main advantages are high interpretability and precision of the results. However, it has other drawbacks such as the modeling of linguistic information by using solely single linguistic terms or the impossibility to model the experts’ hesitancy. Recently, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) linguistic representation model was introduced in order to overcome existing drawbacks in terms of interpretability and accuracy in CW processes. This model allows to model experts’ hesitancy and, at the same time, carry out precise linguistic computations and provide interpretable results, overcoming the limitations of previous LDM models. Therefore, this contribution presents an updated version of FLINTSTONES able to manage ELICIT information in LDM problems and which integrates the ELICIT CW approach.

Decision Making by People With Aphasia: A Comparison of Linguistic and Nonlinguistic Measures

Purpose Decision making involves multiple cognitive and linguistic processes. The extent to which these processes are involved depends, in part, on the conditions under which decision making is assessed. Because people with aphasia (PWA) have impaired language abilities and may also present with cognitive deficits, they may have difficulty during decision-making tasks. Yet little research exists on the decision-making abilities of PWA. Thus, the purposes of this study were to investigate the performance of PWA on linguistic and nonlinguistic decision-making measures and to explore the relationship between decision making and cognitive test performance. Method A quasi-experimental design was used to compare the performance of PWA ( n = 16) and age- and education-matched control participants ( n = 16) on three decision-making tasks: Making a Decision subtest from the Functional Assessment of Verbal Reasoning and Executive Strategies (linguistic decision-making task), Iowa Gambling Task (nonlinguistic decision-making task with ambiguity), and Game of Dice Task (nonlinguistic decision-making task without ambiguity). Participants also completed assessments of language, working memory, and executive functions. Scores on the three decision-making tasks were compared between groups, and cognitive influences on decision-making performance were examined using correlation analyses. Results PWA differed significantly from control participants on linguistic decision making, particularly when required to verbalize their rationale for making their decision. PWA and control participants did not differ significantly on measures of nonlinguistic decision making. Performance on multiple cognitive measures was correlated with performance on the linguistic reasoning task, as well as one of the nonlinguistic tasks (Game of Dice Task). Conclusions Decision-making tasks that are heavily dependent on language, such as those used in capacity assessments, may disadvantage PWA. Assessments of decision-making capacity should include communication supports for people with acquired communication disorders; further investigation in the areas of decision making and aphasia is needed.

The Dominance Degree-Based Heterogeneous Linguistic Decision-Making Technique for Sustainable 3PRLP Selection

This study develops a novel dominance degree-based heterogeneous linguistic decision-making technique for identifying the most sustainable third-party reverse logistics providers (3PRLPs) under complex input environments. First, qualitative and uncertain inputs that arise from real-world 3PRLP evaluation process are successfully managed by using linguistic terms, hesitant fuzzy linguistic terms, and probabilistic linguistic term sets with different granularities. Then, the dominance degrees of each 3PRLP related to the other 3PRLPs are calculated based on a new ratio index-based probabilistic linguistic ranking method and the dominance matrix is constructed. Furthermore, to represent the closeness of each 3PRLP to the ideal solution, we propose a sort of measures including the dominance-based group utility measure, the dominance-based individual regret measure, and the dominance-based compromise measure. Accordingly, the selection results of 3PRLPs are obtained according to these measures. Finally, the developed method is applied to a case study from car manufacture industry, and the comparison analysis shows that the proposed method is reliable and stable for dealing with the problem of the 3PRLP selection. The main advantage of the developed method is that it cannot only well avoid the potential loss risks but also balance group utility scores and individual regret scores.