Linguistic Distribution Research Articles

Classification-based consensus model considering quantum interference in linguistic distribution environment

Multi-attribute group classification (MAGC) is widely used in dealing with a variety of practical problems. However, cognitive biases, resulting from factors such as irrational behaviors, epistemic uncertainty, and interference phenomena, may critically affect the consensus reaching process (CRP) of MAGC. To eliminate such influences, this study proposes an ordinal classification consensus model in a linguistic environment based on quantum probability theory (QPT), with the aim of classifying the alternatives into several predefined ordinal classifications accepted by a majority of experts. First, the linguistic distribution assessments (LDAs), which can depict the quantitative distribution and qualitative vagueness, are used to express the uncertain preference of experts. Second, the LDAs best-worst method (BWM) and LDAs maximizing deviation model (MDM) are developed to identify the subjective and objective weight of attributes, and then these two types of attributes weight are integrated by quantum-like Bayesian networks (QLBN). Third, a maximum consensus model (MCM), maximizing the consensus level of ordinal classification results of experts, is provided to determine the parameters of QLBN. When the consensus level is lower than the predetermined threshold, a minimum adjustment distance model (MADM) is designed to guide the preference-modification of experts to derive the consensus classification of alternatives. Finally, a risk assessment case study is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis and comparison analysis are performed to verify the feasibility and effectiveness of the proposed method.

Minimum adjustment cost consensus model with bounded risk preference attitudes: A perspective based on fractional stochastic dominance

Linguistic multi-attribute group decision making with linguistic distribution assessment (LMAGDM-LDA) problems have been widely investigated. However, in the existing studies, the risk attitudes of decision maker are seldom considered. Inspired by this, in this paper, we employ the idea of fractional stochastic dominance to develop a consensus reaching framework for LMAGDM-LDA with bounded risk preference attitudes. Then, we design an interactive learning algorithm for obtaining the bounded risk preference threshold. Next, we analyze the deception behaviors and average behaviors of experts, and develop the minimum adjustment cost consensus model with bounded risk preference attitude (MACC-BRP). And we justify the connection between the MACC-BRP and the minimum adjustment cost consensus model with other risk attitudes. Furthermore, we provide a distance-based method to obtain the rankings of alternatives. Furthermore, a numerical analysis is provided to show the computation process of the proposed method, and a comparison analysis and a simulation analysis are further conducted to show the advantages and features of the proposal. The simulation results show the effects of the risk preference threshold, the intrinsic individual weight and the average threshold. The comparison results show that the proposed minimum adjustment cost consensus models have clear advantages.

A group stability-based consensus model for multi-criteria group decision-making problems with linguistic distribution assessments

In multi-criteria group decision-making (MCGDM) problems with linguistic distribution assessments (LDAs), this study proposes a group stability-based consensus model to facilitate the generation of solutions that satisfy predefined group stability and group consensus requirements. The group stability and group consensus measures are constructed at the criteria, alternative, expert, and global levels. In terms of different degrees of group stability and group consensus in MCGDM problems, it can be categorized into four situations by comparing with predefined thresholds, which are: 1) group stability measure is satisfied, and group consensus measure is satisfied; 2) group stability measure is not satisfied, and group consensus measure is satisfied; 3) group stability measure is satisfied, and group consensus measure is not satisfied; 4) group stability measure is not satisfied, and group consensus measure is not satisfied. To accelerate the group stability-based group consensus reaching process, optimization problems with group stability and group consensus requirements are constructed to obtain suggested intervals and recommended intervals for LDAs. This contributes to the feedback mechanisms corresponding to the four different situations. The proposed method is applied to an ultrasonic medical imaging workstation selection problem to demonstrate its effectiveness. The superiority of the proposed method is validated through comparative experiments.

Sakha Language in a Northern Industrial Monotown: Linguistic Distribution in Speech Repertoire of Sakha People

This study explores the functioning of the Yakut language in the conditions of the northern industrial monotown Mirny (Republic of Sakha (Yakutia)). The study identifies the features of linguistic distribution in the speech repertoire of ethnic Sakha based on data from a sociolinguistic survey and interviews (n=279). Descriptive statistical methods and content analysis are employed. The results reveal an asymmetric nature of Yakut-Russian bilingualism with Russian language predominance in public communication. While Sakha individuals exhibit a high level of language competence in the Yakut language, its application is limited. In familial and friendly communication, the Yakut language maintains significant positions; however, its demand is minimal in the professional-business sphere, especially in industry. Among individuals with higher education, the percentage of Russian speakers at work reaches 68.34% compared to 55.77% among those with only secondary vocational education. Russian language dominates in management, service sectors, and law enforcement (75—100%). Only in fields such as healthcare, education, and culture does the Yakut language maintain strong positions. The native language is primarily perceived by Sakha as a symbolic marker of identity, yielding to Russian in social prestige and communicative power. The vitality prospects of the Yakut language in the city are assessed ambiguously and are linked to the effectiveness of language policy in key institutional contexts (education, media, government).

A Consensus-Based 360 Degree Feedback Evaluation Method with Linguistic Distribution Assessments

The 360 degree feedback evaluation method is a multidimensional, comprehensive assessment method. Evaluators may hesitate among multiple evaluation values and be simultaneously constrained by the biases and cognitive errors of the evaluators, evaluation results are prone to unfairness and conflicts. To overcome these issues, this paper proposes a consensus-based 360 degree feedback evaluation method with linguistic distribution assessments. Firstly, evaluators provide evaluation information in the form of linguistic distribution. Secondly, utilizing an enhanced ordered weighted averaging (OWA) operator, the model aggregates multi-source evaluation information to handle biased evaluation information effectively. Subsequently, a consensus-reaching process is established to coordinate conflicting viewpoints among the evaluators, and a feedback adjustment mechanism is designed to guide evaluators in refining their evaluation information, facilitating the attainment of a unanimous evaluation outcome. Finally, the improved 360 degree feedback evaluation method was applied to the performance evaluation of the project leaders in company J, thereby validating the effectiveness and rationality of the method.

Trends in Communication Strategies through Traditional Medicine: Responding to the COVID-19 Pandemic in Tanzania

This study sought to establish trends in communication strategies through traditional medicine in Tanzania. The study employed the descriptive content analysis design as it was crucial for gaining insights into how language and communication strategies impact the utilization of traditional medicine in the context of COVID-19 management in Tanzania. The study involved a comprehensive documentary review of COVID-19 communication materials used in Tanzania to establish how language and communication strategies impact the utilization of traditional medicine in the context of COVID-19 management in Tanzania. Data was analyzed through the qualitative content analysis approach.The study concludes that COVID-19 communication materials made commendable efforts to reach the diverse linguistic communities in Tanzania, taking into account the country's linguistic diversity. Kiswahili and English emerged as the dominant languages, which aligns with the linguistic distribution of the population. This approach promotes inclusivity and ensures access to information. The materials demonstrated effective translation and interpretation, highlighting a commitment to accurate translation and cultural sensitivity. It is advisable to incorporate multiple languages, including local languages, to make the campaigns more accessible and relatable to linguistically diverse communities in Tanzania. Secondly, it is imperative to maintain clear communication strategies that empower individuals with varying levels of health literacy. Furthermore, when discussing traditional remedies, adopting an objective approach and stressing the significance of consulting healthcare providers for well-informed decision-making is essential. Moreover, the strategic use of visual elements should be employed to ensure a consistent and well-structured presentation of crucial information.

Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction

To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into two types based on their content: one reflects the effectiveness of an alternative implementation and the other reflects the public expectations. Secondly, the two types of public opinions are sorted separately by linguistic variables. The evaluation of alternatives and the evaluation of expectations in different attributes are determined, both of which are expressed in the form of linguistic distributions. These two evaluations are then compared to determine the public satisfaction of the attributes in different alternatives. Thirdly, based on the deviation of public satisfaction in different attributes, a weight optimization model is constructed to determine the attribute weights. Fourthly, leveraging the interval credibility of attribute satisfaction for various alternatives, an evidential reasoning non-linear optimization model is established to obtain the comprehensive utility evaluation value for each alternative, which is used for ranking. Finally, a numerical example is employed to validate the feasibility and effectiveness of the proposed approach. According to the results of the numerical example, it can be concluded that the proposed approach can be effectively applied to large group decision-making problems that consider public satisfaction. Based on the comparison of methods, the proposed approach has certain advantages in reflecting public opinions and setting reference points, which can ensure the reliability of the decision results.

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

Enhancing reliability of failure modes and effects analysis dealing with linguistic distribution assessments: A consistency based approach

Failure modes and effects analysis is a proactive reliability-management engineering technique, it has been applied to improve reliability of processes, products, and services. Traditional failure modes and effects analysis method manifests three deficiencies with regard to form of risk assessments, determination of risk factor weights, and exploitation of failure modes’ risk levels. Although numerous studies have contributed to surmount these deficiencies, existing failure modes and effects analysis methods still face such issues. Moreover, a novel drawback has raised when failure modes and effects analysis participants adopt linguistic terms as risk assessments of failure modes, the reliability of linguistic distribution assessments as well as failure modes and effects analysis groups (participants with similar knowledge background are previously classified into a failure modes and effects analysis group) is overlooked. Therefore, this paper introduces the transformation of linguistic distribution assessments into fuzzy preference relations, as more effective modeling for investigating consistency, by using the newly defined possibility degrees of linguistic distribution assessments will be applied to devise four algorithms for enhancing reliability of assessments. Hence, the three deficiencies of traditional failure modes and effects analysis method are solved by means of a novel failure modes and effects analysis framework. Finally, a real example with respect to ranking failure modes of worm wheel grinding machine is developed to verify the performance and advantages of the proposed method, in which ten failure modes are ranked and the one with the highest risk level is selected, and consistency of risk assessments is improved to be acceptable.

New distance measure-driven flexible linguistic consensus model with application to urban flooding risk assessment

As a novel linguistic representation tool, flexible linguistic expression (FLE) has substantial flexibility in expressing ambiguous and uncertain information from experts. However, FLEs pose the following challenges to developing information measurement and consensus mechanisms for FLEs-based multi-attribute group decision-making (MAGDM). (1) The current studies primarily use linguistic distribution approximation or triangular fuzzy numbers to measure the difference between FLEs indirectly without proposing an exact distance measure. (2) Few studies have been conducted on direct accurate consensus models incorporating adaptive consensus feedback mechanisms. To deal with these issues, the flexible linguistic ordinal Deng entropy (FLODE) is firstly proposed to measure the uncertainty of FLEs, and by combining it with linguistic scale functions, a new distance measure for FLEs is constructed, which can accurately measure the level of group consensus and the difference in pairwise FLEs. Subsequently, by integrating the defined FLODE with the classical entropy weight method, a new method for determining the experts’ personalized attribute weights is constructed, fully considering the impact of experts’ personalized attribute weights on decision-making results. Also, a new consensus model incorporating a minimum adjustment feedback mechanism and dynamic personalized attribute weights is developed, in which the feedback adjustment coefficients and personalized attribute weights are dynamically updated during the consensus reaching process (CRP). Lastly, an urban flooding risk assessment is applied to confirm the effectiveness of the proposed consensus model.

Matrilineal and Patrilineal Genetic Continuity of Two Iron Age Individuals from a Pazyryk Culture Burial

In this study, geographic and linguistic distributions of contemporary and ancient matches with the
 paternal and maternal lineages of two individuals exhumed from the exemplary Pazyryk culture burial site of Ak-Alakha-1 mound 1 were investigated. Using the shared paternal and maternal haplotypes observed in both ancient individuals, extensive database and literature searches were conducted revealing numerous full matches among contemporary Eurasians, majority of whom speak Altaic Languages. Despite the current focus on the two Pazyryk individuals, a rare glimpse into the ancient migrations was gained through the discovery of paternal and maternal haplotype matches across an immense geography that spans from Yakutia to Turkey. In addition to a vast array of archaeological findings in such Scythian “frozen graves” across Central Asia, accumulating archaeogenetic data are expected to shed light on the anthropology of these otherwise mysterious people.

Canonical triangular interval type-2 fuzzy linguistic distribution assessment EDAS approach with its application to production supplier evaluation and ranking

The evaluation and ranking of suppliers is a critical challenge because it profoundly influences the profitability and competitive positions of manufacturers and producers. This paper presents a canonical triangular interval type-2 fuzzy linguistic distribution assessment based on the distance from average solution technique (CTriIT2F-LDA-EDAS), which is a novel method for evaluating and ranking production suppliers using linguistic distribution assessment (LDA) techniques. The CTriIT2F-LDA uses a canonical triangular interval type-2 fuzzy number (CTriIT2FN) to simplify the trapezium (or trapezoidal) interval type-2 fuzzy number (TrIT2FN) typically used in LDA, thereby making it less intricate and more readily adaptable due to its resemblance to the triangular fuzzy number. This paper presents calculation methods for both the CTriIT2FN and LDA, which involve a de-fuzzification function, distance measurements, and entropy measurements. Moreover, the proposed method was implemented to assess production suppliers involved in the production of electric vehicle DC charging stations for the State Grid Corporation of China. A sensitivity analysis that used both expert and criterion weights was performed. Subsequently, a comparative analysis with existing methods was conducted to validate the versatility and efficacy of the method. The results emphasize the critical factors that influence supplier competitiveness, such as environmental protection training, emissions, safety standards, inventory capacity, and minimum order requirements. Consequently, there is now a pressing need for policies focused on fostering environmental training and expanding both production and inventory management capabilities. The evaluation presented here is used to confirm the adaptability and efficiency of the CTriIT2F-LDA-EDAS method.

Application of focus theory of choice in large scale multi-criteria group decision making

Some risky multi-criteria group decision making problems include payoff and probability information. To deal with these problems, this study introduces a large scale multi-criteria group decision making model based on focus theory of choice. In this model, a group of experts’ linguistic evaluations on multiple criteria are first collected to form linguistic distributions. The positive foci of the linguistic distributions are computed and aggregated into the alternatives’ scores. It is noted that in this process the linguistic terms and probabilities are aggregated by using different rules. The positive foci of the alternatives’ scores are computed and the optimal alternative is selected. A pollution treatment evaluation problem is solved by using the proposed model, and simulation experiments and comparative analysis are given.

Three-dimensional dynamic trust-driven consensus model for social network group decision-making with application to sustainable supplier selection in a circular economy

The trust degree between individuals plays an important role in social network group decision-making (SNGDM). The majority of current literature assumes that the trust degree between individuals is constant. However, the trust values among decision-makers (DMs) are subject to change over time. Thus, it is necessary to identify potential dynamic trust that is compatible with the actual SNGDM to support the consensus reaching. For this purpose, this article investigates consensus building that considers dynamic trust among DMs in SNGDM with linguistic distribution assessments (LDAs). Firstly, the three-dimensional trust degree among DMs is constructed from three perspectives: the trust relationship from social networks, the confidence level of DMs, and the similarity of DMs' preferences. Secondly, an optimization model is developed with the objective of maximizing consensus to determine how trust degree is assigned to each perspective, thereby determining the weights of DMs. Then, a double feedback-based consensus mechanism incorporating both opinion evolution and trust evolution is developed. Under the guidance of consensus mechanism, an improved SNGDM approach with LDAs is presented. Finally, we demonstrate our proposed approach through a case study of sustainable supplier selection in a circular economy. Comparative analysis and sensitive analysis verify our approach’s effectiveness.

An optimization method for handling incomplete and conflicting opinions in quality function deployment based on consistency and consensus reaching process

Quality Function Deployment (QFD) is a highly regarded customer-oriented quality management tool, and its key step is to obtain the importance ratings of Customer Requirements (CRs) from customer opinions. However, customers usually provide incomplete and conflicting opinions due to cognitive limitations and differences in backgrounds. To this end, this study integrates an optimization-based consensus reaching process into QFD for dealing with conflicting customer opinions, where customer opinions are modeled with incomplete linguistic distribution assessments. First, a consistency-driven optimization model is designed to convert incomplete linguistic distribution assessments into complete linguistic distribution assessments by maximizing the consistency index of complete linguistic distribution assessments. Then, consensus reaching process based on a two-stage-based consensus model is developed to yield the consensual collective linguistic distribution assessments, which are further utilized to derive the consensual importance ratings of CRs. The first stage of the consensus model aims to minimize the adjustment between the original linguistic distribution assessments and the adjusted linguistic distribution assessments, and the second stage aims to minimize the symbolic distance between the original linguistic distribution assessments and the adjusted linguistic distribution assessments. Finally, a case study of product development for EVCARD in China, a sensitivity analysis, and a comparative analysis are presented to show the effectiveness and feasibility of our proposal.

Consensus reaching with heterogeneous stochastic dominance in the enterprise credit rating under linguistic distribution assessments context

Consensus reaching with heterogeneous stochastic dominance in the enterprise credit rating under linguistic distribution assessments context

Tracking global English changes through local data: Intensifiers in German Learner English

Aims and objectives/purpose/research questions: The empirical goal of the paper is to document the linguistic distribution and patterning as well as the sociolinguistic conditioning of intensifiers in English spoken by educated young adults from Germany. Here, I also seek to understand how the empirical data presented here informs our understanding of the mechanisms underlying language change in those forms of English that emerge through the combined impact of naturalistic L2 acquisition and instructed foreign language learning (henceforth, EFL or Learner English). Design/methodology/approach: The study reports intensification data from 53 advanced learners of English from Germany and compares it with that reported for L1 English vernaculars. The data were elicited with the help of sociolinguistic interviews tapping into learners’ natural linguistic behaviour. The processes of language change are explored against the backdrop of grammaticalisation theory, as espoused by variationist sociolinguists. Data and analysis: The study employs two methods of analysis: distributional percentages and fixed-effects logistic regression analyses. These allow pinpoint rates and patterns of intensification as well as its language-internal and language-external conditioning. Findings/conclusions: Systematic comparisons of language-internal conditioning underlying the occurrence of linguistic variants pinpoint ‘functional specialisation’ (also ‘entrenchment’) as a driving mechanism of and a potent constraint on language change. Originality: While demonstrating how functional specialisation is operative in the system of German Learner English intensification, the present study highlights the relevance of EFL data to empirical testing and advancement of (socio-)linguistic theory. The study also highlights the relevance of learners’ linguistic identity and the sociopsychological construct of ‘relatedness’ in the adoption of the globally available features of the English language. Significance/implications: The study pleads for more research at the intersection of historical/general linguistics and SLA as such an approach adds to knowledge about language acquisition, language variation and, ultimately, language change.

A novel robustness PROMETHEE method by learning interactive criteria and historical information for blockchain technology-enhanced supplier selection

In a complex transaction and operation environment, supply chain usually faces the disruption risk, especially the supplier selection. Fortunately, the blockchain technology not only can provide traceability and decentration for supply chain management, but also can increase supply chain resilience. With the aid of blockchain technology, this paper deeply proposes a two-stage performance measure model to assess suppliers reinforcing supply chain resilience. Firstly, we screen out the assessment criteria of supply chain and blockchain by using complex network. Subsequently, for depicting the hesitation of multiple experts, we formulate the generation mechanism of linguistic distribution. Besides, in the blockchain technology-enabled supply chain, the criteria of supply chain and blockchain are interactive. Therefore, considering the interactive criterion, we combine the PROMETHEE method with the internal interactive index to measure supplier performance in the first stage. Meanwhile, the robustness PROMETHEE method is further improved by designing three types of aggregation models. In the second stage, we also concern the supply chain situation before adopting blockchain technology. In this case, this paper further designs a correction method of supplier performance measure based on supplier historical information in order to select the reliable supplier. Finally, we verify the effectiveness and rationality of our proposed method. In general, this study enriches the evaluation criteria of supplier performance measure by considering the influence of blockchain technology and provides the correspond decision support for blockchain technology-enabled supply chain management.

A three-way decision model associated with decision-theoretic rough fuzzy sets based on computing with words

The three-way decision model based on linguistic term sets has been extensively investigated since decision makers frequently utilize natural language to evaluate in an actual decision-making process. The existing models require decision makers to select appropriate linguistic terms from a given linguistic term set. However, making such a choice is not always simple, and decision makers occasionally choose words that are related to their own experience. In order to deal with this kind of decision problem, we appeal to the theory of computing with words pioneered by Zadeh and establish a three-way decision model based on computing with words in this paper. The paper focuses on how to deal with more general linguistic information using the theory of computing with words. Initially, using the concept of computing with words, we translate more broad linguistic information into a linguistic distribution assessment on a balanced linguistic term set in order to better analyze linguistic information. The three-way decision based on computing with words is then discussed. Decision-theoretic rough fuzzy sets take into account the ambiguity of the decision target as a generalization of the classical decision-theoretic rough sets. This is what motivated us to develop a three-way decision based on decision-theoretic rough fuzzy sets using computing with words. Additionally, a fabricated example demonstrates that our three-way decision model is more adaptable in processing linguistic information and can handle more general linguistic information provided by decision makers.

Managing Overconfidence Behaviors From Heterogeneous Preference Relations in Linguistic Group Decision Making

The linguistic information of decision makers (DMs) often implies individual preferences and behavioral traits. Reaching consensus in linguistic group decision making (GDM) requires understanding the overconfidence behaviors exhibited by DMs and implementing effective management. However, owing to the differences in individual expression and understanding, the management of the overconfidence behaviors of DMs must effectively handle heterogeneous preference information and personalized individual semantics (PIS). To solve this problem, this study manages overconfidence behaviors exhibited by DMs in linguistic GDM by considering PIS and multiple self-confidence levels. Specifically, DMs utilize flexible linguistic representation models to provide preference values over alternatives and corresponding self-confidence levels, thereby generating heterogeneous preference relations with self-confidence (PRs-SC). We then integrate heterogeneous preference relations into unified linguistic distribution expressions. In addition, we transform linguistic PRs-SC into additive PRs-SC using a PIS model. We detect and manage individuals' overconfidence behaviors to decrease the negatively impact of overconfidence on consensus efficiency and the decision quality. Subsequently, DMs' preference values and self-confidence levels are utilized to generate modification suggestions to achieve a consensus. Finally, we use a loan selection on a peer-to-peer lending platform as an example to illustrate and demonstrate the usability, effectiveness, and feasibility of our model through a simulation and comparative analysis.