Linguistic Preference Research Articles

Linguistic acculturation preferences of Catalan local students toward their peers of Moroccan descent. The determining factors of linguistic assimilationism

ABSTRACT As a response to the migratory movements initiated at the end of the past century in Catalonia (Spain), its educational system aims to promote interculturality and multilingualism as a way to achieve social cohesion. For this purpose, it is important to improve our understanding of the linguistic acculturation preferences endorsed by local students toward students of immigrant origin – a scarcely studied factor. One of the main factors for achieving these objectives, that has been scarcely studied, is the linguistic acculturation preferences endorsed by local students toward students of immigrant origin. The present study applied 349 questionnaires to analyze the determining factors and linguistic acculturation preferences of local high-school students regarding the linguistic acculturation profiles of their peers of Moroccan descent in Lleida (Catalonia, Spain). The main results showed that, in order of frequency, assimilation to Catalan, assimilation to Spanish-Catalan and multilingualism were the three linguistic preferences endorsed by local students. The multinomial logistic regression model found the following determining factors: attitudes toward minority languages, attitudes toward multilingualism, Spanish self-identification, Spanish use with teachers and Catalan use with classmates. These results highlight the importance and need to further promote the use and embracement of minority languages in school settings as a possible way to increase the construction of multilingual preferences, and deconstruct prejudices and stereotypes to ultimately achieve social cohesion.

Managing Missing Preference Values Through Consistency and Consensus in Distributed Linguistic Preference Relations: A Two-stage Method Based on Personalized Individual Semantics

Managing Missing Preference Values Through Consistency and Consensus in Distributed Linguistic Preference Relations: A Two-stage Method Based on Personalized Individual Semantics

Consistency Improvement With a Feedback Recommendation in Personalized Linguistic Group Decision Making.

Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided.

A Granular Computing-Driving Hesitant Fuzzy Linguistic Method for Supporting Large-Scale Group Decision Making

Considering the conditions that: 1) same linguistic term means different things for different people; 2) flexible semantics cannot be represented by original linguistic term; and 3) some semantics given by decision makers are possible to be changed during the consistency improving process, we bring some flexibility and personality into hesitant fuzzy linguistic preference matrix structures by allowing the linguistic preference matrices to be granular rather than numeric, providing a new characterization of linguistic preference matrices. Inspired by the thought of granular computing, this article proposes a new hesitant fuzzy linguistic method to deal with issues when a lot of decision makers provide hesitant and uncertain preference information in the decision-making process. First, we design a multiplicative consistency index and calculate its thresholds corresponding to different dimensions of preference matrix by the Monte Carlo experiment. Then, we construct a hesitant fuzzy linguistic model with granularity level, so as to recharacterize original assessment information and improve the consistency of preference matrices as far as possible. Considering the features of some large-scale group decision-making situation, where the decision makers have little opportunity to take part in multiple consensus reaching processes, hesitant fuzzy linguistic fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> -means clustering algorithm is developed to integrate the assessment information given by decision makers. Finally, the final decision-making results are derived. An illustrative example of assessing psychological situation of some COVID-19 infected persons clarifies the reasonability of the proposed method. Finally, we complete some comparative studies and simulation experiments to demonstrate the method’s validity and advantages.

An Interactive Consensus Model in Group Decision Making with Heterogeneous Hesitant Preference Relations

This paper proposes an interactive consensus reaching model in the group decision making for heterogeneous hesitant preference relations (i.e., hesitant fuzzy preference relations, hesitant multiplicative preference relations, hesitant fuzzy linguistic preference relations). First, the consistencies of three hesitant preference relations are defined, respectively. Then, based on their definitions, three optimization models are constructed to obtain the weight vector of alternatives, based on which an interactive consensus adjustment algorithm is established based on the direct consensus framework. This framework adopts feedback mechanism to facilitate the information correction of decision makers. After several rounds of adjustment, the decision results with satisfactory consensus level are achieved. Finally, the practicability and effectiveness of the model are illustrated through a case study of mine accident emergency decision making.

К вопросу о чебоксарском шибболете

The work is devoted to the study of the shibboleth in the speech culture of the inhabitants of the capital of Chuvashia. A shibboleth is a word that has absorbed the specific features of a language and is used to identify people for whom this language is not native. The relevance of the study is due to the very existence of the phenomenon of shibboleth in the everyday life of the residents of Cheboksary, which has not yet been studied from the point of view of linguocheboksary. The purpose of the study is to formulate a language unit that maximally concentrates the specific features of the Russian speech of the inhabitants of the city of Cheboksary. The material of the study was the speech samples of Cheboksary residents, during the quantitative and qualitative analysis of which a shibboleth was derived in the form of an interrogative phrase “Skazhite, pozhaluysta, nasha konferencia budet zavtra?” (Tell me, please, will our conference be tomorrow?). At the same time, checking with a shibboleth involves the task of transforming the specified phrase into the Cheboksary regiolect of the Russian language. Completing the task according to the linguistic preferences prevailing in Cheboksary should lead to the invariant “Skazhite-ka, u nas konferencia zavtra budet, chto li?”. The author of the work simultaneously considers Russian shibboleths aimed at the Chuvash linguistic culture, and vice versa. In the Chuvash environment, words with a concentration of a kind of Chuvash phonetics can act as a shibboleth aimed at Russians. In turn, the reproduction of the Russian shibboleth in the Chuvash environment, due to the developed Chuvash-Russian bilingualism, practically does not present any problem in modern realities.

A novel integrated MADM method for design concept evaluation

Design concept evaluation plays a significant role in new product development. Rough set based methods are regarded as effective evaluation techniques when facing a vague and uncertain environment and are widely used in product research and development. This paper proposed an improved rough-TOPSIS method, which aims to reduce the imprecision of design concept evaluation in two ways. First, the expert group for design concept evaluation is classified into three clusters: designers, manufacturers, and customers. The cluster weight is determined by roles in the assessment using a Multiplicative Analytic Hierarchy Process method. Second, the raw information collection method is improved with a 3-step process, and both design values and expert linguistic preferences are integrated into the rough decision matrix. The alternatives are then ranked with a rough-TOPSIS method with entropy criteria weight. A practical example is shown to demonstrate the method’s viability. The findings suggest that the proposed decision-making process is effective in product concept design evaluation.

Multiplicative consistency of linguistic preference relations with weakened hedges

Preference relations are often used to derive the priority of attributes and/or alternatives. Linguistic term with weakened hedges (LTWH) as a type of complex linguistic expressions can more straightforwardly describe the linguistic information provided by DMs when evaluating under uncertainties. The preference relations represented by LTWHs are an effective tool to model linguistic information. The concept and properties of additive consistency have been proposed before. This paper aims to study the multiplicative consistency of preference relations expressed by LWTHs. This paper constructs the principle of inspection for multiplicative consistency. Especially, the theories and algorithms for consistency checking and improving are proposed. We develop an automatic approach to improve a LWHPR that is not multiplicatively consistent. Finally, we demonstrate the practicality of the proposed method through a case study of evaluating the attributes in the prevention of haze pollution in China.

Hesitant fuzzy linguistic iterative method for consistency and consensus-driven group decision making

Hesitant fuzzy linguistic information has been used as a powerful expression to present the judgments under uncertainty. To improve its decision-making theory and enhance its application ability, we propose a decision-making method that considers the individual acceptable consistency and the group consensus of hesitant fuzzy linguistic preference relations (HFLPRs). Firstly, we define a multiplicative consistency of a HFLPR and make hypothesis testing analyses for the consistency threshold. Then, we establish a group consensus procedure to achieve comprehensive decision-making results that possibly reserve the original judgments of decision makers. Some experiments are conducted to show the values of the consistency threshold in different scenarios, and make support for using the proposed method. An illustrative example is finally given to perform the method.

A hesitation-feedback recommendation approach and its application in large-scale group emergency decision making

Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards.

Developing a quality function deployment model for the Ethiopian leather industry: Requirements and solutions under linguistic variables

ABSTRACT This study develops a quality function deployment model that integrates requirements and solutions through a qualitative information approach to break the current low-quality trap and ensure the improvement of the leather products manufacturing industry. This industry is a priority sector in Ethiopia’s industrial development strategy, but its performance is below par due to quality-related problems. Prior studies lack a model explaining the pathways to implement quality improvement interventions. The improvement of this industry requires qualitative assessment and is subject to uncertainty due to the linguistic preferences of decision makers. This study adopts the fuzzy Delphi method to arrive at a valid set of attributes and a fuzzy quality function deployment method to address their relationships. The findings demonstrate that 22 requirements and 16 solutions are valid measures. The top solutions include ensuring interdisciplinary design, employing process mapping, implementing quality-related standards and customer-centric operations, and establishing a quality-at-the-source attitude. Theoretical and practical implications are discussed.

Personalized Individual Semantics-Based Consistency Control and Consensus Reaching in Linguistic Group Decision Making

Consistency and consensus are important issues for linguistic group decision making (GDM), which have been extensively studied by scholars. Nevertheless, most of previous consensus reaching models focus on adjusting decision makers’ preference relations and ignore the individual consistency, which results in that individual consistency may be destroyed by using these consensus reaching models. Moreover, it has been accepted that words mean different things for different people and thus, it is also necessary to model decision makers’ personalized individual semantics (PISs) in linguistic GDM. This work focuses on developing some PIS-based consistency control and consensus reaching models for linguistic GDM. First, we analyze the problems existing in previous PIS models and then develop a minimum adjustment-based optimization model to test and improve the individual consistency for a linguistic preference relation (LPR). Followed by this, a PIS-based individual consensus-level maximization model and a PIS-based minimum adjustment model are established for consensus reaching in linguistic GDM, in which individual consistency control is considered. Furthermore, an algorithm for consensus reaching is proposed based on these models. To justify the proposed models and algorithm, some numerical results and simulation analysis are provided eventually.

Learning personalized individual semantics through the data of distributed linguistic preference relations: A two-stage method to support linguistic consensus reaching

Distribution linguistic preference relation (DLPR) is an effective tool to model linguistic preferences with multiple linguistic terms, and has widespread adoption in the linguistic group decision making (GDM), in which we commonly accept the existence of personalized individual semantics (PISs). Currently, the existing research on PISs is driven by a consistency-based method and assumes the fixed PISs for decision makers. However, decision makers may update their PISs during the consensus reaching process (CRP), which implies the importance of learning PIS from the preference data. To analyze the changing PISs in the CRP in a linguistic GDM problem with DLPRs, we put forward a PIS-learning-based two-stage consensus approach by considering all data of DLPRs given by decision makers. The PIS-learning-based two-stage consensus approach uses the consistency-driven and consensus-driven methodology to update the personalized numerical scales (PNSs), and guarantee the acceptable consistency and consensus simultaneously. Finally, we apply the proposed method to the financial technology selection. Moreover, a comparative analysis verifies the effectiveness of the PIS-learning-based two-stage consensus approach for supporting GDM with DLPRs.

Promoting the Adoption of Agricultural Green Production Technologies for Sustainable Farming: A Multi-Attribute Decision Analysis

Stakeholders have become worried about the environmental problems of agricultural production activities. Therefore, there is pressure on smallholder farmers to observe environmental regulations and embed sustainable green technologies in their production. The literature on green production has thoroughly emphasized the critical role of behavioral factors in adopting environmental sustainability practices. We develop a probabilistic linguistic preference selection index method to assess the behavioral constructs that promote the adoption of agricultural green production technologies (AGPTs) among smallholder rice farmers in an emerging economy. The result shows that the five most-important factors promoting the adoption of AGPTs include knowledge (0.828), perceived cost and benefit (0.819), descriptive norm (0.810), moral and environmental concern (0.809), and injunctive norm (0.807). The study findings offer insightful directions for examining rice farmers’ decisions on the adoption of AGPTs. Our findings imply that policymakers should consider multiple behavioral factors when designing policies that promote AGPTs. This study enriches farmers’ adoption decisions by modeling the uncertainties in the decision-making process.

A new linguistic preference relation-based approach for failure mode and effect analysis with dynamic consensus reaching process

In the literature, a lot of alternative failure mode and effect analysis (FMEA) models have been developed to overcome the deficiencies and enhance the performance of traditional FMEA method. However, majority of them evaluated the risk of failure modes directly and cannot take the consensus levels among experts into account. Thus, this article aims to propose a new approach for FMEA via integrating hesitant linguistic preference relations (HLPRs) and an extended dynamic consensus model. Based on the HLPRs, the risk evaluations of experts are described via pairwise comparison of the identified failure modes. An extended dynamic consensus model is proposed to measure the weights of FMEA experts and revise the individual preference information until reaching an acceptable consensus. Finally, the applicability and effectiveness of the proposed FMEA are verified with a practical case study on the risk analysis of supercritical water gasification systems. It was shown that the new approach being proposed provides a practical and flexible way for the risk assessment and prioritization of failure modes in FMEA.

Revisiting Additive Consistency of Hesitant Fuzzy Linguistic Preference Relations

Consistency has always been a hot topic in the study of decision-making based on preference relations. This paper focuses on the consistency of hesitant fuzzy linguistic preference relations (HFLPRs). Firstly, a new definition of the additive consistency of HFLPRs is given. Secondly, to examine whether an HFLPR is additively consistent, two equivalent programming models are constructed. Thirdly, for inconsistent HFLPRs, the corresponding consistency improvement model is further proposed, where only upper triangular elements in the HFLPRs are considered in view of the symmetry of HFLPRs. Using the consistency improvement model, an inconsistent HFLPR can be adjusted to the consistent one, which retains the original information as much as possible. Fourthly, a hesitant fuzzy linguistic weight vector is introduced and a programming model is constructed to derive the weight vector. Finally, the feasibility and effectiveness of the proposed method are illustrated by numerical examples and comparative analysis. This result demonstrates that the consistency model proposed considers each element of HFLPRs such that the consistent HFLPRs derived fully retain the original information. Moreover, only some preference values in the HFLPR are adjusted, and no preference value is out of range of the predefined HFLTSs.

Integrated fuzzy linguistic preference relations approach and fuzzy Quality Function Deployment to the sustainable design of hybrid electric vehicles

Through the prevalence of sustainable ideas, automobiles are increasingly pursuing environmental protection strategies for green design, the non-traditional hybrid electric vehicles (HEV) are promoted continuously. If the company can add emotional value to the modeling of HEV, it will be helpful to its sustainable design and sales promotion of it. Therefore, an innovative model combining fuzzy linguistic preference relations (FLPR) and fuzzy quality function deployment (QFD) is proposed here to explore the connection between customer sentiment and the front view of the HEV. Compared with the previous methods, FLPR has the advantages of fewer comparison times and high consistency. First, find out the customer’s emotional expectations and attribute weight ranking for HEV through FLPR, and import customer requirements (CRs) on the left side of fuzzy QFD. Second, the grey prediction model was used to screen out the key engineering features (ECs) and the initial weight of HEV. Finally, based on human subjective imprecise natural semantics, fuzzy QFD established a matrix association between CRs and key ECs, then finally obtained the optimal combination of ECs' final weight and morphological design. The results can assist designers to shorten product development cycles and improve customers' emotional satisfaction, which provides a theoretical reference for the sustainable design and marketing of environmentally friendly cars in the future.

Consistency improvement under a personalized individual semantics context in distributed linguistic group decision making

Consistency measurement is a significant issue in linguistic decision making when preferences are expressed via linguistic preference relations. However, the extant literatures on linguistic consistency generally overlook the fact that even the same word can have diverse meanings for different people, which indicates that people usually possess personalized individual semantics (PISs) over words. Furthermore, with the complexity of the practical decision-making problem increases, decision makers become more likely to be uncertain and hesitant to make their preferences due to the lack of knowledge, therefore, their linguistic preferences may be represented through distributed linguistic representations. However, there are few consistency improving studies on distributed linguistic representations. Therefore, in this study we devise a novel consistency improving approach for distribution linguistic preference relations under a PISs context. Furthermore, the usability and effectiveness of the PISs based consistency improvement method are verified through the detail numerical analysis and comparative study.

Consistency and consensus reaching process for group decision making based on complete interval distributed preference relations under social network analysis

Group decision-making (GDM) problems often consist of many indeterminacy factors in realistic situation. How to cope with consistency and consensus under uncertain circumstance are two critical issues in pairwise comparison based GDM problems. In this paper, we firstly propose the model of complete interval distributed preference relation (CIDPR) based on the concept of linguistic distribution with interval symbolic proportions, distribution linguistic preference relation (DLPR) and IDPR. Secondly, the additive consistency index of CIDPR is defined to measure the consistency level of expert's judgment, and an adjustment algorithm is proposed for converting inconsistent CIDPR to an acceptable consistent level. Thirdly, since trust relation is a critical factor in the generation of experts’ weights and the adjustment of experts’ opinions, consensus reaching process (CRP) is designed to take into account distributed linguistic trust relations under social network analysis (SNA). In the proposed adjustment mechanism, non-consensus individual should modify opinion towards his/her trusted and highly weighted expert. The advantage of the proposed inconsistent CIDPR adjustment model can maximally retain the information in the original distribution, while the CRP has a relatively fast convergent speed and good practicality. An illustrative example of strategic new product selection is conducted to demonstrate the applicability of the proposed method and its potential in supporting realistic GDM problems.

Minimum information-loss transformations to support heterogeneous group decision making in a distributed linguistic context

In the problems that linguistic assessments are conducted by adopting multiple sources of information representations, the management of unification of heterogeneous information and information loss are necessary. To support a useful fusion of heterogeneous distributed information in linguistic group decision making, a minimum information-loss transformation framework is proposed in this paper. First, distributed linguistic distance measurements are defined to measure information loss among heterogeneous distributed linguistic preference information, and then several minimum information-loss transformation models (MILTMs) with desirable properties are proposed. Furthermore, the application of the MILTMs in addressing the fusion of heterogeneous distributed linguistic information in a multi-attribute group decision context is discussed, and the flexibility of distributed linguistic information is studied to justify the MILTMs through numerical examples and comparative analyses.